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Your Complete Guide to Pursuing a PhD in Robotics: Scope, Schools & Careers

Embarking on a PhD in robotics positions you at the intersection of technology and innovation, opening pathways to academia, private sector research, and cutting-edge industry applications. In this straightforward guide, delve into the essential aspects of a Robotics PhD—including the scope, exemplary schools, and the promising careers it can lead to, ensuring you make an informed decision about your academic future.

Key Takeaways

A PhD in Robotics is an interdisciplinary program that blends computer science, engineering, and other related fields, focusing on cutting-edge research in areas such as machine learning, human-robot interaction, and autonomous systems, which prepares graduates for diverse careers in academia, research, or the private sector.

Top universities offering esteemed PhD programs in Robotics include Carnegie Mellon University, Massachusetts Institute of Technology, and Stanford University, known for their outstanding faculty, comprehensive curricula, and innovative research opportunities.

The application process for a PhD in Robotics typically requires a bachelor’s or master’s degree in a related field, standardized test scores like the GRE, English proficiency tests for non-native speakers, recommendation letters, a statement of purpose, and in some cases, a resume.

Understanding PhD in Robotics: An Overview

Robotic technologies, now integral to our societies, are shaping a technological revolution. A PhD in Robotics provides the skills necessary to lead in this field. This program is an interdisciplinary blend of:

computer science

electrical and computer engineering

mathematics

mechanical engineering

This interdisciplinary approach, which includes systems engineering, offers students a comprehensive understanding of robotics, preparing them to contribute to a variety of sectors.

A key aspect of a doctoral program in Robotics is the focus on cutting-edge research. Some areas of research in these programs include:

Enhancing machine learning algorithms

Advancing human-robot interaction

Developing autonomous systems

Improving computer vision and perception

Designing and controlling robotic systems

These programs encourage students to push the boundaries of existing knowledge and technology.

The career opportunities post a PhD in Robotics are diverse and exciting. Graduates have the choice to pursue academia, engage in research, or utilize their skills within the private sector. From healthcare to manufacturing and autonomous vehicles, the demand for robotics expertise is growing, offering promising career prospects to Robotics PhD graduates.

Interdisciplinary Nature

The Robotics PhD program epitomizes the interdisciplinary study. It incorporates principles of computer science, engineering, and other related disciplines, creating a holistic view of robotics. This unique blend of disciplines fosters innovation and collaboration, empowering students to explore intricate problems and contribute to the advancement of robotics.

Research Focus

Research forms the backbone of any PhD program, and Robotics is no exception. The program motivates students to investigate various areas such as artificial intelligence, machine learning, and computer vision. The objective is to equip students with the ability to conduct in-depth research, enabling them to solve complex problems and further advance knowledge in the field of robotics.

Career Opportunities

A PhD in Robotics opens a gateway to a plethora of career opportunities. Graduates can pursue:

Academic roles such as researchers and professors

Contribute to research and development in the private sector

Venture into sectors like healthcare and manufacturing.

As digitization proliferates worldwide, the need for robotics expertise escalates, suggesting that a Robotics PhD is a promising career pathway.

Top Universities Offering PhD Programs in Robotics

Several esteemed universities offer a PhD program in Robotics. Some of the notable institutions include Carnegie Mellon University, Massachusetts Institute of Technology (MIT), and Stanford University. These universities, celebrated for their outstanding faculty, broad curriculum, and pioneering research initiatives, are excellent choices for aspiring Robotics PhD students.

Carnegie Mellon University

Carnegie Mellon University’s Robotics Institute offers a renowned PhD program in Robotics. The program is recognized for its:

Interdisciplinary nature

Cutting-edge research initiatives

Faculty composed of eminent individuals

Cooperative opportunities for students

Empowerment of students to promote robotics advancement

Massachusetts Institute of Technology (MIT)

MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) is distinguished for its Robotics PhD program. The program is recognized for its focus on robotic hardware and algorithms that incorporate sensing, control, perception, and manipulation. The distinguished faculty members guide students through their research journey, fostering innovation and excellence in robotics.

Stanford University

Stanford University’s Artificial Intelligence Laboratory offers a unique Robotics PhD program. The program presents unique research opportunities in areas like humanoid robots, bio-inspired robots, and cooperative robots. Guided by a team of esteemed faculty members, students are encouraged to push the boundaries of robotics research and contribute to its advancement.

Admission Requirements and Application Process

The admission requirements for a Robotics PhD program typically include:

A bachelor’s or master’s degree in a related field

Relevant coursework

Standardized tests like the GRE

TOEFL or IELTS for non-native English speakers

However, it’s important to note that the specific requirements may vary across universities.

The application process for a Robotics PhD program usually involves:

Submitting transcripts

Submitting recommendation letters

Submitting a statement of purpose

Some universities may also require a resume

Every university has unique application procedures and deadlines, thus checking the respective university’s website for precise information is necessary.

Prerequisites

Applicants for a Robotics PhD program usually need:

A strong foundation in mathematics

Proficiency in computer science, including programming and data analysis

Research experience, although not necessarily in the field of robotics, enhances the application.

A solid academic performance, demonstrated through a minimum undergraduate GPA requirement, is also often a prerequisite for students in their second or third year.

GRE Scores and Standardized Tests

GRE scores and other standardized tests are integral to the admission process for Robotics PhD programs. However, these scores are evaluated in conjunction with other elements such as GPA, recommendation letters, and essays.

For non-native English speakers, proof of English proficiency through TOEFL or IELTS scores, which assess verbal skills, may also be required.

Supporting Documents

Supporting documents such as:

Transcripts

Letters of recommendation

A statement of purpose

are crucial components of a Robotics PhD application. These documents provide a comprehensive picture of the applicant’s academic journey, achievements, and research capabilities, aiding the admissions committee in making an informed decision.

Curriculum and Coursework

The curriculum of a Robotics PhD program typically includes:

Core courses that provide a solid foundation in robotics, encompassing mechanics, controls, perception, artificial intelligence, and human-robot interaction

Electives that allow students to specialize in specific areas of robotics

A significant research component that allows students to conduct original research in the field of robotics

This comprehensive curriculum is designed to provide students with a deep understanding of robotics and prepare them for careers in academia, industry, or research.

Elective courses allow students to delve deeper into specific areas of interest, enabling them to specialize in a particular aspect of robotics. The research component, a significant part of the program, permits students to undertake independent study and promote knowledge in the robotics field.

Core Courses

The core courses in a Robotics PhD program cover fundamental topics such as:

Artificial Intelligence

Human-Robot Interaction

These courses provide the students with a comprehensive understanding of the field, equipping them with the necessary skills to engage in advanced research and pursue a master’s degree.

Elective courses in Robotics PhD programs offer the opportunity to focus on specific areas of interest. These courses equip students with specialized knowledge and skills, enabling them to conduct focused research in their chosen area. Some examples of areas of focus in Robotics PhD programs include:

Computer vision

Human-robot interaction

Artificial intelligence

Machine learning

Control systems

Autonomous systems

By taking elective courses in these areas, students can deepen their understanding and expertise in their chosen field of study.

Research Components

The research component of a Robotics PhD program typically involves independent study, laboratory work, and a dissertation. This component allows students to apply the knowledge and skills gained from their coursework to solve complex problems, contributing to the advancement of robotics.

Online and Part-Time Options for Robotics PhD Programs

In addition to traditional full-time programs, many universities offer online and part-time options for Robotics PhD programs. These flexible options provide an opportunity for those who wish to balance their studies with work or other responsibilities.

Online Programs

Some universities, like Capitol Technology University, offer fully online PhD programs in Robotics. These programs provide the flexibility to study from any location, making them ideal for individuals who cannot commit to on-campus studies.

Part-Time Study

Part-time study options for Robotics PhD programs offer the following benefits:

Students can balance their education with work or other responsibilities

The programs retain the rigorous curriculum of their full-time counterparts

The coursework is extended over an extended duration

Funding Opportunities and Financial Aid

Pursuing a PhD in Robotics is a significant financial commitment. However, there are various funding opportunities and financial aid options available to help offset the cost. These include fellowships, grants, and teaching or research assistantships.

Fellowships and grants, providing financial assistance, can help alleviate tuition and research expenses. Teaching and research assistantships, aside from offering financial support, provide worthwhile experience to refine students’ skills and elevate their career possibilities.

Fellowships

Fellowships offer the following benefits to Robotics PhD students:

Financial aid to offset tuition costs and research expenses

Opportunity to engage in independent research

Contribution to the advancement of knowledge in the field of robotics

Grants and Scholarships

Grants and scholarships are another form of financial aid available to Robotics PhD students. These funding options can help cover tuition costs and research expenses, making the pursuit of a PhD more accessible.

Teaching and Research Assistantships

Teaching and research assistantships provide financial support and valuable experience for Robotics PhD students. These assistantships involve assisting in teaching or research activities, providing a practical perspective to the theoretical knowledge gained through coursework. Research advisors play a crucial role in guiding students through these assistantships.

Real-World Applications and Impact of Robotics Research

Robotics research has significant real-world applications, impacting various sectors from healthcare to manufacturing and autonomous vehicles. Robotics advancements carry the potential to transform these sectors, boosting efficiency and productivity.

In healthcare, robotics research has led to the development of surgical robots and assistive devices, improving patient care and treatment outcomes. In manufacturing, robotics has enabled automation and improved efficiency. And in the realm of autonomous vehicles, advancements in AI, machine learning, and computer vision have paved the way for self-driving cars.

In the healthcare sector, Robotics research has led to significant advancements. Surgical robots have enhanced the efficiency and precision of medical procedures. Assistive devices have improved the quality of life for individuals with disabilities. These developments have revolutionized patient care, making healthcare more accessible and effective.

Manufacturing

In the manufacturing industry, robotics research has enabled automation of laborious tasks, leading to increased efficiency and productivity. Robots can handle repetitive and physically strenuous roles, allowing for greater precision and reduced likelihood of human error. These advancements have led to improved quality control and lower production costs.

Autonomous Vehicles

Autonomous vehicles are an exciting application of robotics research. Advances in AI, machine learning, and computer vision have enabled vehicles to navigate autonomously, understanding their environment and making informed decisions.

These developments could transform transportation, rendering it safer and more efficient.

Pursuing a PhD in Robotics offers the opportunity to contribute to a field that is shaping the future. This program equips students with a comprehensive understanding of robotics, preparing them to address complex problems and advance knowledge in this exciting field. With the interdisciplinary nature of the program, the focus on cutting-edge research, and the wide range of career opportunities, a PhD in Robotics is a promising choice for those interested in this dynamic field.

The advancements in robotics are revolutionizing various sectors, from healthcare to manufacturing and autonomous vehicles. As the demand for robotics expertise grows, the opportunities for Robotics PhD graduates are expanding, making it an exciting time to embark on this academic journey. So, are you ready to step into the future and make your mark in the world of robotics?

Frequently Asked Questions

Can you get a phd in robotics.

Yes, you can get a PhD in robotics, and one example of such a program is offered jointly by the College of Computing and the College of Engineering at Georgia Institute of Technology.

How long is a PhD in robotics?

A PhD in robotics typically takes about five to six years to complete. The program includes coursework, a research qualifier, and the submission of a thesis.

What is the starting salary for robotics PhD?

The starting salary for a robotics PhD can range from $83,500 to $127,000 annually in the United States, with some top earners making up to $156,000 annually.

Why PhD in robotics?

A PhD in robotics will provide graduates with a diverse skill set encompassing mathematical thinking and technological proficiency, positioning them for careers in technology design, programming, and equipment maintenance in the field of robotics.

What are the prerequisites for a Robotics PhD program?

To apply for a Robotics PhD program, you’ll need a bachelor’s or master’s degree in a related field, relevant coursework, and a strong background in mathematics and computer science. These are the typical prerequisites for admission.

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PhD Thesis: A Unified Design Framework for Mobile Robot Systems

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• Doctor of Philosophy

A PhD in Robotics from the University of Colorado Boulder will elevate you to the highest levels of your chosen field, to make lasting and significant impacts to fundamental knowledge, technology, and society through research. As a PhD student, you will be become a domain expert and complete research that can withstand the rigorous test of external peer review. Graduates from the PhD program go on to careers in industry, academia, and the public sector, and will be leaders in their respective fields.

The PhD program in Robotics is available to students who are entering graduate studies for the first time (i.e., with only a BS or BA degree), as well as to those who already have a master’s (MS) degree. Many incoming PhD students will have prior degrees in some type of engineering or computer science, although students from other fields, such as physics, mathematics, biology, and chemistry, are also routinely admitted and can acquire any missing background material during the course of their PhD studies at CU Boulder.

A PhD student entering without prior graduate coursework will typically take five years to complete the PhD degree. However, it is not uncommon for students to finish both earlier and later than this five-year average. A student entering the PhD program with prior graduate coursework from another university may be eligible to transfer up to 21 credit hours to CU and can typically finish in 3-4 years.

Program Requirements

Pre-comprehensive exam student status.

  Post-comprehensive exam student status

Course Requirements

You must complete a minimum of 30 graduate-level credits at the 5000 level or higher. All PhD students are required to fulfill the requirements of the Master of Science - Non-Thesis program.

Some research advisors require their students complete more than 30 course credits. The graduate program recommends that, in addition to your graduate advisor, you consult with your research advisor regarding any coursework recommendations or requirements.

In order to receive credit towards the PhD, you must receive a grade of at least B- in each course taken. Courses in which a grade below B- is achieved cannot be counted towards the PhD course requirement. Courses taken on a P/F basis cannot count towards the PhD course requirement.

You must have a cumulative 3.0 GPA in order to be eligible for graduation. However, a 3.25 GPA is required to be eligible for Teaching or Research Assistantships.

Transfer Credit

Although you do not need an MS degree to be admitted to the PhD program, students who already have an MS degree, or have completed eligible graduate level coursework, may transfer up to 21 hours of credits towards the PhD course requirements.

More information is available on the second page of the Request for Transfer of Credit Form from the CU Graduate School. To transfer credits, you must fill out and submit this form to your graduate advisor at with official transcripts included.

Note that requests for transfer credit can only be made after completing 6 credits of graduate level coursework at CU Boulder. These requests should be submitted as soon after completion of this 6 credit requirement as possible.

Oral Preliminary Exam

All PhD students must pass the oral preliminary exam, the goal of which is to assess fundamental and practical preparation for a research-intensive program. As such, the exam combines a “research” component with a “fundamentals” component to provide a holistic picture of your preparation.

The research component consists of a short research-focused presentation on a research topic you could initiate, followed by questions from the exam committee. This part of the exam requires you to demonstrate your ability to synthesize primary sources, identify and communicate technical gaps in the relevant literature, and articulate a sound research plan.

The fundamentals component consists of up to four questions pertaining to two student-selected subject area core courses. This part assesses your knowledge consolidation, internalization, and retention in relation to core course material, which should provide a foundation for future learning and research. Both components also allow the exam committee to assess a your on-the-spot technical communication skills early in your career. 

Based on your performance, the preliminary exam committee will provide an evaluation of pass, conditional pass, or fail. If the result is a conditional pass, the committee may require you to retake a portion of the exam or to complete another condition that displays fundamental proficiency. If you fail a preliminary exam, you will either be asked to retake the exam in full at the next opportunity, or may be asked to leave the PhD program. Students who fail a preliminary exam twice will be asked to leave the PhD program.

While not recommended, students are allowed to “retake” the exam on different topics, but they would only have a single opportunity to pass.

Comprehensive Examination

You must complete a comprehensive exam at least 6 months prior to defending your PhD dissertation. At the time of the comprehensive exam, the dissertation committee will be formed and given preliminary approval by the Department and Graduate School.

A robotics PhD degree requires depth of knowledge in the dissertation/research area, as well as breadth of knowledge across the robotics curriculum. Consequently, the comprehensive exam is designed to test your knowledge of your proposed research area, and any general knowledge in the field. It is also intended to evaluate whether your proposed research project is original and creative work, whether it will make a significant impact in the field, and whether it will qualify for publication in quality peer-reviewed journals. The exam is also an opportunity to demonstrate an ability to present scientific concepts orally. In short, the comprehensive exam serves as the gateway to the next phase of the doctoral program: completion of a dissertation.

The comprehensive exam consists of the following core requirements:

• Complete the online Candidacy Application for Advanced Degree at least three weeks prior to your exam date.
• Student ID Number
• Committee member name
• Committee member email address
• Committee member department affiliation
• By email, send the comprehensive exam proposal to (i) the examining committee and (ii) the graduate advisors at [email protected] at least two weeks prior to the examination. The proposal should describe the work that has been completed to date and proposed work that will be completed for the dissertation.
• Included in the proposal should be a comprehensive literature review of the field of concentration, the subject of the dissertation, as well as a detailed timeline of work to be completed prior to the dissertation defense. In most cases, the proposal should be written in the style and format of the final dissertation document.
• You must prepare a professional oral presentation that covers what is written in the proposal. This presentation should be 45-50 minutes in length and must be delivered at the comprehensive examination to the examination committee. The oral presentation portion of the examination is open to all students and faculty, and questions are entertained at the end of the presentation.
• The final part of the examination is restricted to only the student and the examination committee. During this portion, questions are entertained that cover the field of concentration and related fields.
• Successful candidates must receive affirmative votes from a majority of the members of their examina-tion committee.

Students who fail the examination may attempt it once more after a period of time determined by the examination committee. Additional administrative requirements of the comprehensive examination are as follows:

• All program coursework must be completed before taking the comprehensive exam.
• Students must be registered as regular degree-seeking students when they take the comprehensive exam (thus requiring a minimum enrollment of 1 dissertation credit hour).
• Each comprehensive exam committee is comprised of five members. The program requires that three of the members be robotics program faculty.
• Each committee member must have a regular or special faculty appointment on file with the Graduate School prior to submission of the Doctoral Exam Report. Please contact the graduate advisors at [email protected] as soon as you form your committee, and no later than 6 weeks prior to your comprehensive examination, to verify that the necessary appointments are in place. It takes 2-4 weeks to process a faculty appointment. Students should submit a recent CV for any committee member who does not have a faculty appointment to the graduate advisors as soon as possible.

Dissertation Hour Requirement

In addition to coursework, PhD students are required to complete 30 PhD dissertation hours. Students are not able to register for dissertation credits on their own and should submit a request for dissertation hours through the Thesis/Dissertation Credit Hours Request Form.

The following Graduate School rules apply to enrollment in dissertation hours and should be considered when determining how many dissertation hours to register for each semester:

• PhD students must be registered as full time, regular degree-seeking students at CU Boulder for a minimum of 5 dissertation hours during the semester in which they defend the dissertation.
• A student may not register for more than 10 dissertation credit hours in any one semester, including summer.
• A PhD student is required to register continuously as a full-time student for a minimum of five disser-tation hours in the Fall and Spring semesters of each year, beginning with the semester following the passing of the comprehensive examination and extending through the semester in which the dissertation is successfully defended.
• Prior to passing the comprehensive exam, PhD students are considered by the Graduate School to be full-time if they are registered for at least 1 dissertation credit per semester.

There is little advantage to registering for more than 30 dissertation hours during the course of your PhD, and so students should attempt to complete this requirement in the semester in which they defend. Please contact the graduate advisors at [email protected] for assistance with planning dissertation hour enrollment.

Written Dissertation

The written dissertation must comply with Graduate School rules and procedures in terms of format and submission. Full details on formatting requirements are available on the Graduate School website, and deadlines and resources to assist in finalizing your dissertation are available on the Doctoral Graduation Page. You can also find both Word and LaTeX templates for written dissertations online.

The dissertation title appears on official university transcripts and must be submitted to the Graduate School in addition to the physical signature page from the dissertation. You are also required to submit the full written dissertation electronically at the ProQuest website. The timeline for these requirements is as follows:

• Final dissertation title submission is due about two months into the final semester.
• The oral dissertation defense must be passed shortly after this date.
• The written dissertation; and
• The Thesis Approval Form, signed by the faculty advisor and one additional committee member.

Dissertation Defense

Before completion of the PhD degree, you must have their dissertation accepted for defense by the review committee. The dissertation defense may occur before or after the final electronic submission of the written dissertation to the Graduate School, but must take place prior to the end of the final semester of enrollment.

You must then pass a dissertation defense, which is a final examination on the dissertation and related topics. In the defense, you are expected to explain their research clearly and concisely, and to discuss how it relates to other research in the field. This is an opportunity for recognition of completed doctoral work. It is also an opportunity for discussion and formal evaluation of the dissertation.

All required forms should be submitted on time according to the following deadlines:

• Student ID number
• To the Committee: The written dissertation should be sent as a single pdf file by email to all members of the defense committee, as well as to the graduate advisors at [email protected] , at least 2 weeks before the defense. This deadline is intended to allow the defense committee sufficient time to review the dissertation and to formulate questions and feedback. Prior to the defense, you should contact all members of the committee to assess their areas of interest and concerns. This will help you anticipate any questions that will be asked.

You must be registered as full time, regular degree-seeking students at CU Boulder for a minimum of 5 dissertation hours during the semester in which you pass the examination. The examination is conducted by a committee appointed by the chair of the Robotics Program and approved by the Dean of the Graduate School, and consists of at least five people with the following requirements:

• One committee member must be outside the Robotics Program;
• Three of the members must be Robotics Program faculty.

The chair of the committee must have tenure-track or tenured Graduate Faculty appointments. The other committee members must have either regular or special Graduate Faculty appointments. More than one dissenting vote disqualifies the candidate in the final examination.

You should coordinate scheduling the examination with the committee, and should schedule the examination for two hours. The examination is wholly oral and open to the public for the first portion of the examination.

You must prepare and present a professional oral presentation that summarizes the dissertation. This presentation should be 45-50 minutes in length and delivered to the examination committee. The oral presentation portion of the examination is open to all students and faculty. Questions are entertained at the end of the presentation.

The final part of the examination is closed to only the student and the examination committee. During this portion, questions are entertained that cover the field of concentration and related fields. More than one dissenting vote among the committee constitutes an unsatisfactory exam. A student who fails the exam may attempt it once more after a period of time determined by the committee.

PhD Student Status

As the requirements towards the PhD degree are completed, PhD students will advance from pre-preliminary exam, to post-preliminary exam, to post-comprehensive exam, status. Milestones required to achieve each status are the following:

• Pre-comprehensive exam status (Pre-comps): Students enter the PhD program with pre-comps status and will typically remain at this status until successful completion of the comprehensive exam in year 4. Students should complete the required course (ROBO 5000: Intro to Robotics) and oral preliminary exam during this time.

Application for Graduation

To graduate with the PhD degree, you must complete all course and dissertation hour requirements, as well as write and defend your dissertation. Additional details on each of these requirements are provided above.

To graduate with the PhD degree, you must apply online through Buff portal. Directions to complete this process can be found on the Registrar’s website.

The application for graduation is due a few weeks after the start of the desired graduation semester. Full details on requirements and deadlines can be accessed on the Graduate School PhD graduation webpage. If you did not submit the Candidacy Application for Advanced Degree when completing the comprehensive examination, it must be submitted electronically prior to applying for graduation online.

PhD students must be registered as a full time, regular degree-seeking student, for a minimum of 5 dissertation hours during the semester in which they pass the final exam. If you are unable to meet the Graduate School’s posted defense deadline for that semester, you should consult with your graduate advisor about graduation options.

Detailed graduation information will be communicated to all students through the graduate student listserv at the beginning of each semester.

Master’s Degree as a PhD Candidate

Although a Master’s degree is not required for a PhD, you can earn one while working toward the PhD. This is accomplished by applying for an MS degree when 30 graduate course hours have been completed. All Master of Science requirements must be completed in order to receive the MS degree. PhD students must notify their graduate advisor within the first two weeks of the semester in which they intend to graduate with the MS degree.

• Master of Science - Non-Thesis
• Master of Science - Thesis

This is a list of the PhD and PDEng theses of (former) PhD and PDEng candidates that have done their research (partly) at RaM. Included are the theses for which a RaM member was the promotor.

Note: Due to the name change of the faculty from EWI to EEMCS this list is currently incomplete. The university is working on this and we hope to see it fixed soon.

RaM laboratory

Faculty of Electrical Engineering, Mathematics & Computer Science University of Twente

P.O. Box 217 7500 AE Enschede, Netherlands   Phone: +31 (0)53 489 2626

Objectives of the program

The EDRS program provides doctoral students with the education necessary to be leaders in multi-disciplinary areas at the intersection of computer science, electrical engineering, and mechanical engineering.

Description

The program focuses on innovative methodologies to synthesize, analyze, and control cyber-physical systems of increasing complexity finding application in a variety of domains, including health, built and natural environment, mobility, energy, pedagogy, human-machine interaction, and manufacturing.

Students gain in-depth knowledge and competences in their specific research area as well as a solid background in system-oriented science and engineering.

You look to…

• share, develop and realize innovative ideas in Robotics, working with a team of world-class experts
• share and develop ideas for production methodology that respect environmental and energy resources by interacting with fully experienced shop floor engineers
• share, develop and construct innovative medical instruments to enhance patient comfort

…  we then have common dreams, ambitions and concerns.

Before applying, candidates should consult the websites of the laboratories participating in the Robotics, Control and Intelligent Systems Program to get acquainted with current research activities and to identify potential thesis directors. You can find a link to the list of EDRS thesis directors here .

Upcoming public defense

Advanced entry Ph.D.

The advanced entry Ph.D. is for students with an M.S. in an engineering discipline or equivalent field.

Direct Ph.D.

The direct Ph.D. is for students entering the program with a B.S. in an engineering discipline or equivalent field.

For a comprehensive overview of the programs, including degree requirements, please consult the most recent handbook

Ph.D. candidate Remesh Shrestha, co-advised by Professors Sheng Shen and Maarten de Boer, explains his research to create polymer nanowires that have high thermal conductivity:

Other Ph.D. programs and partnerships

Apply here (by these deadlines).

For spring 2023

For fall 2022

The application for fall entry opens in October.

More information

Ph.D. employment stats

Ph.D. enrollment and completion stats [pdf]

• google scholar

I am a Research Scientist in the Seattle Research Lab (SRL) at NVIDIA Research led by Dieter Fox. Previously, I received my Ph.D. from the Robotics Institute at Carnegie Mellon University. I obtained my B.A.Sc. in Engineering Science from University of Toronto, with a major in Aerospace Engineering and a minor in Robotics and Mechatronics.

I am generally interested in reasoning about human intention and behavior given arbitrary task spaces for the purposes of continuous human robot interaction. Currently exploring reasoning and interaction for manipulators. Previously, I worked on human intention modeling and prediction for continuous human-in-the-loop control of aerial and ground robots and agile safe navigation in unknown and unstructured environments. My areas of focus are robot system design, navigation, task representation, human robot interaction.

You can contact me at xuningy at nvidia dot com.

• Human-in-the-loop Planning for Mobile Robots PhD Thesis, Robotics Institute, Carnegie Mellon University, Jan 2022 [ bibtex ] [ pdf (33.4MB) ] [ talk ]

Publications

Continuous Dynamic Autonomy via Path Prediction on Semantic Topological Maps Xuning Yang , Jean Oh Submitted to the International Conference on Intelligent Robots and Systems (IROS), 2022 [ pdf ] [ video ]

An imminent collision monitoring system with safe stopping interventions for autonomous aerial flights Jasmine Cheng, Xuning Yang , and Nathan Michael ICRA 2021 Workshop on “Resilient and Long-Term Autonomy for Aerial Robotic Systems”, Spotlight Talk [ bibtex ] [ pdf ]

Online adaptive teleoperation via motion primitives for mobile robots Xuning Yang , Ayush Agrawal, Koushil Sreenath, and Nathan Michael Special Issue on Learning for Human-Robot Collaboration, Autonomous Robots, April 2018 [ bibtex ] [ pdf ]

Online Adaptive Teleoperation via Incremental Intent Modeling Xuning Yang , Koushil Sreenath, and Nathan Michael Late Breaking Report, International Conference on Human-Robot Interaction (HRI), March 2017 [ bibtex ] [ pdf ]

Other projects

I enjoy building robots. Some of the robots that are near and dear to my heart are listed here.

Rocky with GPS: Modifications for outdoor flights

Rocky: Compact, agile quadrotor with onboard compute

Rocky Lite: Minimally compact and agile quadrotor

• College of Computing

Ph.D. in Robotics

The Institute for Robotics and Intelligent Machines (IRIM ) serves as the flagship for Tech’s robotics efforts and therefore, the research institute has an integral relationship with the program. Almost all of IRIM faculty members serve as research advisors to students pursuing the robotics degree.

The program supports Tech’s mission to provide education in disciplines related to science, technology, and interdisciplinary areas, and to recruit and educate outstanding students who will provide leadership in a world that is increasingly dependent on technology. Currently, Tech has more than 40 faculty members actively engaged in the Ph.D. robotics program.

Admission Requirements

The Georgia Tech criteria used in determining each applicant’s eligibility for consideration includes:

• A bachelor’s degree or its equivalent (prior to matriculation) from a recognized institution; graduation in the upper quarter of their class; students must show evidence of preparation in their chosen field sufficient to ensure profitable graduate study;
• GRE scores (General Test is required for all; Subject Tests in Computer Science, Math or Physics recommended but not required);
• For international applicants, satisfactory scores on the Test of English as a Foreign Language (TOEFL). Minimum scores are 100 (Internet-based test), 250 (computer-based) or 600 (paper-based).

Students enroll for the Robotics Ph.D. Program through one of the participating units:

• Aerospace Engineering
• Biomedical Engineering
• Electrical and Computer Engineering
• Mechanical Engineering

Students should indicate that they are applying for the Robotics Program through that unit by marking a check box. As minimum requirements, students must satisfy all of the specific admission requirements of the home unit.

The Robotics Ph.D. Program Committee will make final admission decisions in coordination with the home units.

Decisions are based on a combination of factors:

• Academic degrees and records
• Statement of purpose
• Letters of recommendation
• GRE and TOEFL test scores
• Relevant work experience

Also considered is the appropriateness of the applicant’s goals to the Robotics Ph.D. Program, their expected abilities in carrying out original research, and the faculty research interests.

Complete the online application . 

Program of Study

The main emphasis of the  Robotics Ph.D. program  is the successful completion of an original and independent research thesis. The degree requirements are designed around this goal.

Minimum Requirements

• Completion of 36 semester hours of courses with a letter grade
• Passing a comprehensive qualifying exam with written and oral components.
• Successfully conducting, documenting, and defending a piece of original research culminating in a doctoral thesis.

Ph.D. Candidacy

Prior to completing all of these requirements, Georgia Tech defines the Ph.D Candidate milestones. Admission to candidacy requires that the student:

• Complete all course requirements (except the minor);
• Achieve a satisfactory scholastic record;
• Pass the comprehensive examination;
• Submit and receive approval naming the dissertation topic and delineating the research topic.

Core Area Courses

The core areas of robotics consist of: Mechanics, Control, Perception, Artificial Intelligence, Autonomy and Human-Robot Interaction (HRI). They are used to select three foundation courses and three targeted elective courses. Visit phdrobotics.gatech.edu/program for a full list of core area courses.

Qualifying Exam

The purpose of the comprehensive exam is to assess the student’s general knowledge of the degree area and specialized knowledge of the chosen research area. The comprehensive examination provides an early assessment of the student's potential to satisfactorily complete the requirements for the doctoral degree. As such, it requires that fundamental principles be mastered and integrated so that they can be applied to solving problems relevant to robotics.

After three regular semesters (Fall or Spring) from entering the Ph.D. program the student must take the comprehensive examination at the next scheduled offering, usually during the fourth regular semester. If the comprehensive examination is failed, the student may have one additional opportunity at the next scheduled offering. The examination will be offered at least once every year.

The comprehensive exam is a written and oral examination and is administered by a faculty committee, selected by the thesis advisor in consultation with the student, and approved by the Robotics Program Committee. The committee consists of:

• Three faculty members consistent with the student's graduate coursework and research area.
• The thesis advisor as a non-voting observer.

From the Catalog:

Automatic Control, Robotics and Vision

COORDINATOR

• Puig Cayuela, Vicenç

Doctoral Area - Barcelona Industrial Engineering Management and Support Unit (UTGAEIB) Av. Diagonal, 647 08028 Barcelona

Tel.: 934 016 654 E-mail: [email protected]

https://arv.phd.upc.edu

Origin and framework of the programme The doctoral programme in Automatic Control, Robotics and Vision (ARV) was established in 2006 by combining the Advanced Automation and Robotics programme of the Institute of Industrial and Control Engineering (IOC) and the Control, Vision and Robotics programme of the Department of Automatic Control (ESAII), both of which had received quality awards from the Spanish Ministry of Education and Science. The merger was the result of an increasing affinity and convergence in the content and activity of the two programmes and served as an opportunity to adapt to the new system for official postgraduate programmes within the framework of the European Higher Education Area. From the first year that the new doctoral programme was offered, it was recognised with a quality award from the Ministry of Education and Science (code MCD2007-00150, years 2007, 2008, 2009, 2010). The programme has also been granted the Pathway to Excellence award by the Ministry of Education (code MEE2011-0453, valid from 2011-2012 to 2013-2014). The doctoral programme in Automatic Control, Robotics and Vision provides a framework for students to complete doctoral theses in these fields, which are of vital importance in traditional industry and many service applications. Work is carried out mainly under the supervision of professors associated with the programme, using research facilities made available by the two units responsible for the degree (IOC and ESAII); the Institute of Robotics and Industrial Informatics (IRI), which collaborates very actively with the programme; and the research groups involved. The doctoral programme in Automatic Control, Robotics and Vision is a natural continuation of the master's degree in Automatic Control and Robotics (a UPC programme taught in English). Students who complete this master’s degree are therefore eligible for direct admission to the doctoral programme. Students with degrees in related fields may also be considered for admission. The academic committee will require that students admitted with other degrees complete specific bridging courses from the master’s degree in Automatic Control and Robotics (selected on a case-by-case basis) to ensure that they have the knowledge and skills needed to conduct research and complete a doctoral thesis in one of the areas covered by the programme.

Automatic control and robotics play an increasingly important role in contemporary society, from both a social perspective (habits and greater convenience and quality of life) and in terms of their direct and indirect economic significance. Consequently, research and development in this field is of vital importance and marks a clear difference between developed and developing countries. In the latter, products are marketed, or in some cases manufactured (basically due to lower production costs), but there is usually no know-how or capacity to innovate or produce graduates qualified to work in R&D, and this is where the difference lies. Producing professionals who are capable of innovating and working in highly specialised areas with the latest technologies constitutes a clear and direct contribution to our society. Moreover, all developed countries offer doctoral degrees equivalent to the doctoral programme in Automatic Control, Robotics and Vision.

Aim of the programme The aim of the programme is to provide students with rigorous training that builds on knowledge acquired in previous stages of their education and prepares them to undertake a career in scientific and technological research and to innovate in highly specialised areas on advanced aspects of automatic control, robotics and computer vision. The goal is therefore for doctoral students to develop the ability to find innovative solutions by drawing on solid theoretical knowledge and applying new technologies.

General information

Access profile.

The ideal entrance qualification is a master’s degree that covers areas related to the subject areas of the programme: automatic control, robotics and computer vision. (Degree names vary depending on the applicant's country of origin and the university where they completed their previous studies.) Applicants who have completed the master's degree in Automatic Control and Robotics offered by the Universitat Politècnica de Catalunya qualify for direct admission to the doctoral programme in Automatic Control, Robotics and Vision. Applicants who hold a science degree (e.g. in industrial engineering, electronics, mechanics, electromechanics or informatics) may also be admitted to the programme. Students admitted with these qualifications may be required to complete bridging courses, which will be specified by the academic committee for the programme.

Output profile

Doctoral candidates who complete a doctoral degree will have acquired the following competencies, which are needed to carry out quality research ( Royal Decree 99/2011, of 28 January, which regulates official doctoral studies ):

a) A systematic understanding of the field of study and a mastery of the research skills and methods related to the field. b) An ability to conceive, design or create, put into practice and adopt a substantial process of research or creation. c) An ability to contribute to pushing back the frontiers of knowledge through original research. d) A capacity for critical analysis and an ability to assess and summarise new and complex ideas. e) An ability to communicate with the academic and scientific community and with society in general as regards their fields of knowledge in the manner and languages that are typical of the international scientific community to which they belong. f) An ability to foster scientific, technological, social, artistic and cultural progress in academic and professional contexts within a knowledge-based society.

The award of a doctoral degree must equip the graduate for work in a variety of settings, especially those requiring creativity and innovation. Doctoral graduates must have at least acquired the personal skills needed to:

a) Develop in contexts in which there is little specific information. b) Find the key questions that must be answered to solve a complex problem. c) Design, create, develop and undertake original, innovative projects in their field. d) Work as part of a team and independently in an international or multidisciplinary context. e) Integrate knowledge, deal with complexity and make judgements with limited information. f) Offer criticism on and intellectually defend solutions.

Finally, with respect to competencies, doctoral students must: a) have acquired advanced knowledge at the frontier of their discipline and demonstrated, in the context of internationally recognised scientific research, a deep, detailed and well-grounded understanding of theoretical and practical issues and scientific methodology in one or more research fields; b) have made an original and significant contribution to scientific research in their field of expertise that has been recognised as such by the international scientific community; c) have demonstrated that they are capable of designing a research project that serves as a framework for carrying out a critical analysis and assessment of imprecise situations, in which they are able to apply their contributions, expertise and working method to synthesise new and complex ideas that yield a deeper knowledge of the research context in which they work; d) have developed sufficient autonomy to set up, manage and lead innovative research teams and projects and scientific collaborations (both national and international) within their subject area, in multidisciplinary contexts and, where appropriate, with a substantial element of knowledge transfer; e) have demonstrated that they are able to carry out their research activity in a socially responsible manner and with scientific integrity; f) have demonstrated, within their specific scientific context, that they are able to make cultural, social or technological advances and promote innovation in all areas within a knowledge-based society; g) have demonstrated that they are able to participate in scientific discussions at the international level in their field of expertise and disseminate the results of their research activity to audiences of all kinds.

Number of places

Duration of studies and dedication regime.

Duration The maximum period of study for full-time doctoral studies is four years, counted from the date of first enrolment in the relevant programme until the date on which the doctoral thesis is deposited. The academic committee of the doctoral programme may authorise a doctoral candidate to pursue doctoral studies on a part-time basis. In this case, the maximum period of study is seven years from the date of first enrolment in the programme until the date on which the doctoral thesis is deposited. To calculate these periods, the date of deposit is considered to be the date on which the thesis is made publicly available for review.

If a doctoral candidate has a degree of disability equal to or greater than 33%, the maximum period of study is six years for full-time students and nine years for part-time students.

For full-time doctoral candidates, the minimum period of study is two years, counted from the date of an applicant's admission to the programme until the date on which the doctoral thesis is deposited; for part-time doctoral candidates it is four years.

When there are justified grounds for doing so, and the thesis supervisor and academic tutor have given their authorisation, doctoral candidates may request that the academic committee of their doctoral programme exempt them from the requirement to complete this minimum period of study.

Temporary disability leave and leave for the birth of a child, adoption or fostering for the purposes of adoption, temporary foster care, risk during pregnancy or infant feeding, gender violence and any other situation provided for in current regulations do not count towards these periods. Students who find themselves in any of these circumstances must notify the academic committee of the doctoral programme, which must inform the Doctoral School.

Doctoral candidates may request periods of temporary withdrawal from the programme for up to a total of two years. Requests must be justified and addressed to the academic committee responsible for the programme, which will decide whether or not to grant the candidate's request.

Extension of studies If a doctoral candidate has not applied to deposit their thesis before the expiry of the deadlines specified in the previous section, the academic committee of the doctoral programme may, at the request of the doctoral candidate, authorise an extension of this deadline of one year under the conditions specified for the doctoral programme in question.

Dismissal from the doctoral programme A doctoral candidate may be dismissed from a doctoral programme for the following reasons:

• The doctoral candidate submitting a justified application to withdraw from the programme.
• The doctoral candidate not having completed their annual enrolment or applied for a temporary interruption.
• The doctoral candidate not having formalised annual enrolment on the day after the end of the authorisation to temporarily interrupt or withdraw from the programme.
• The doctoral candidate receiving a negative reassessment after the deadline set by the academic committee of the doctoral programme to remedy the deficiencies that led to a previous negative assessment.
• The doctoral candidate having been the subject of disciplinary proceedings leading to their suspension or permanent exclusion from the UPC.
• A refusal to authorise the extensions applied for, in accordance with the provisions of Section 3.3 of these regulations.
• The doctoral candidate not having submitted the research plan in the period established in Section 8.2 of these regulations.
• The maximum period of study for the doctoral degree having ended, in accordance with the provisions of Section 3.4 of these regulations.

Dismissal from the programme means that the doctoral candidate cannot continue studying at the UPC and that their academic record will be closed. This notwithstanding, they may apply to the academic committee of the programme for readmission, and the committee must reevaluate the candidate in accordance with the criteria established in the regulations.

A doctoral candidate who has been dismissed due to having exceeded the time limit for completing doctoral studies or due to an unsatisfactory assessment may not be Academic Regulations for Doctoral Studies Universitat Politècnica de Catalunya Page 17 of 33 admitted to the same doctoral programme until at least two years have elapsed from the date of dismissal, as provided for in sections 3.4 and 9.2 of these regulations.

Legal framework

• Royal Decree 99/2011, of 28 January, which regulates official doctoral studies (consolidated version)
• Academic regulations for doctoral studies (CG/2023/09/08)

Organization

• Casals Gelpi, Alicia
• Catala Mallofre, Andreu
• Doria Cerezo, Arnau
• Griño Cubero, Roberto
• Olm Miras, Josep Maria
• Rosell Gratacos, Jan
• Sanfeliu Cortes, Alberto
• Institute of Industrial and Control Engineering (PROMOTORA)
• Department of Automatic Control
• Institute of Robotics and Industrial Informatics

Agreements with other institutions

• IMT Lille Douai, France (http://imt-lille-douai.fr/)
• Buenos Aires Institute of Technology, Argentina (https://www.itba.edu.ar/)
• Tuxtla Gutierrez Institute of Technology, Mexico (https://www.tuxtla.tecnm.mx/)
• Normandy University, France (http://www.normandie-univ.fr/)
• University of the Andes, Colombia (https://uniandes.edu.co/)
• Aigües de Barcelona (https://www.aiguesdebarcelona.cat/)
• Aingura IIoT (http://www.ainguraiiot.com/)
• BROSE (https://www.brose.com/de-en/)
• FICOSA (https://www.ficosa.com/es/)
• INLOC Robotics, S.L. (https://inlocrobotics.com/es/)
• KIVNON (http://agvkivnon.com/)
• Pal Robotics (http://pal-robotics.com/es/)
• Rücker Lypsa (https://www.rueckerlypsa.es/)
• SEAT (https://www.seat.es/)
• Wide Eyes Technologies (https://wideeyes.ai/)
• National University of Colombia (https://unal.edu.co/). Aims: to facilitate mobility of academic staff participating in joint research activities; to facilitate mobility of master’s and doctoral students. Start year: 2009. Term: 2009-2013
• Meritorious Autonomous University of Puebla (BUAP), Mexico (https://www.buap.mx/). Aim: to facilitate mobility of academic staff and graduate students from BUAP to the UPC. Start year:  2011. Term: 2011-2014
• University of Guadalajara, Mexico (http://www.udg.mx/). Aims: to provide training for doctoral degree holders and researchers; to facilitate mobility of students and academic staff between the two institutions. Start year: 2010. Term: 2010-2013
• University of Naples Federico II, Italy (http://www.unina.it). Aim: to facilitate mobility of students and academic staff for doctoral studies within the framework of the Erasmus programme. Start year: 2011. Term: 2011-2013
• University of Split, Croatia (https://www.unist.hr/en/). Aim: to facilitate mobility of students and academic staff for doctoral studies within the framework of the Erasmus programme.  Start year: 2011. Term: 2011-2013

Access, admission and registration

Access requirements.

As a rule, applicants must hold a Spanish bachelor's degree or equivalent and a Spanish master's degree or equivalent, provided they have completed a minimum of 300 ECTS credits on the two degrees ( Royal Decree 43/2015, of 2 February ).

Applicants who meet one or more of the following conditions are also eligible for admission:

a) Holders of official Spanish degrees or equivalent Spanish qualifications, provided they have passed 300 ECTS credits in total and they can prove they have reached Level 3 in the Spanish Qualifications Framework for Higher Education. b) Holders of degrees awarded in foreign education systems in the European Higher Education Area (EHEA), which do not require homologation, who can prove that they have reached Level 7 in the European Qualifications Framework, provided the degree makes the holder eligible for admission to doctoral studies in the country in which it was awarded. c) Holders of degrees awarded in a country that does not belong to the EHEA, which do not require homologation, on the condition that the University is able to verify that the degree is of a level equivalent to that of official university master's degrees in Spain and that it makes the holder eligible for admission to doctoral studies in the country in which it was awarded. d) Holders of another doctoral degree. e) Holders of an official university qualification who, having been awarded a post as a trainee in the entrance examination for specialised medical training, have successfully completed at least two years of training leading to an official degree in a health sciences specialisation.

Note 1: Regulations for access to doctoral studies for individuals with degrees in bachelor's, engineering, or architecture under the system prior to the implementation of the EHEA (CG 47/02 2014).

Note 2: Agreement number 64/2014 of the Governing Council approving the procedure and criteria for assessing the academic requirements for admission to doctoral studies with non-homologated foreign degrees (CG 25/03 2014).

Admission criteria and merits assessment

There are no specific admission requirements for the doctoral programme in Automatic Control, Robotics and Vision beyond those that apply under current legislation. However, bear in mind that the academic committee for the programme reviews the academic records of all applicants to assess how well their academic background matches the ideal entrance qualifications indicated above. Applicants should have training equivalent to that which students receive on the UPC master's degree in Automatic Control and Robotics. Graduates of this UPC master’s degree qualify for direct admission to the doctoral programme in Automatic Control, Robotics and Vision.

The academic committee for the programme will issue an assessment of applications for admission. The main criteria considered are:

a) Relevance of previous studies and the applicant's academic record (50%) b) Other merits (experience in research work, publications, proficiency in languages, motivation and reasons for embarking on the doctoral degree) (50%)

When appropriate, the academic committee may require that a student take bridging courses as a condition of admission.

Training complements

The academic committee for the programme determines what bridging courses applicants will have to take if admitted. When an applicant seeks admission with the backing of a professor on the programme who will act as their thesis supervisor, the professor concerned may recommend to the academic committee the bridging courses that they consider most appropriate to direct the doctoral student specifically towards topics related to their doctoral thesis work.

Additional training will be based primarily on subjects of the master's degree in Automatic Control and Robotics. However, if there is sufficient justification, a student may take subjects included on other master's programmes or postgraduate courses. This may be deemed appropriate if there are subjects that are particularly relevant to the thesis work an applicant plans to undertake (when this work has been defined at the time of admission). Applicants who have completed a UPC master's degree in Automatic Control and Robotics qualify for direct admission to the doctoral programme in Automatic Control, Robotics and Vision and will not be required to take any bridging courses.

Enrolment period for new doctoral students

The enrolment period for new doctoral students will be in September.

More information at the registration section for new doctoral students

Enrolment period

After the first year, the enrolment period for doctoral students will run from September to mid-October.

More information at the general registration section

Monitoring and evaluation of the doctoral student

Procedure for the preparation and defense of the research plan.

Doctoral candidates must submit a research plan, which will be included in their doctoral student activity report, before the end of the first year. The plan may be improved over the course of the doctoral degree. It must be endorsed by the tutor and the supervisor, and it must include the method that is to be followed and the aims of the research.

At least one of these annual assessments will include a public presentation and defence of the research plan and work done before a committee composed of three doctoral degree holders, which will be conducted in the manner determined by each academic committee. The examination committee awards a Pass or Fail mark. A Pass mark is a prerequisite for continuing on the doctoral programme. Doctoral candidates awarded a Fail mark must submit a new research plan for assessment by the academic committee of the doctoral programme within six months.

The committee assesses the research plan every year, in addition to all of the other activities in the doctoral student activity report. Doctoral candidates who are awarded two consecutive Fail marks for the research plan will be obliged to definitely withdraw from the programme.

If they change the subject of their thesis, they must submit a new research plan.

Formation activities

Activity: Tutorial. Hours: 288. Type: compulsory. Activity: Mobility. Hours: 480. Type: optional. Activity: Assessment based on doctoral student activity report (DAD) and research plan. Hours: 4. Type: compulsory. Activity: Training in information skills. Hours: 1.5. Type: optional. Activity: Research methodology. Hours: 12. Type: optional. Activity: Innovation and creativity. Hours: 8. Type: optional. Activity: Language and communication skills. Hours: 18. Type: optional. Activity: Courses and seminars. Hours: 30. Type: optional. Activity: Publications. Hours: 80. Type: optional.

Procedure for assignment of tutor and thesis director

The academic committee of the doctoral programme assigns a thesis supervisor to each doctoral candidate when they are admitted or enrol for the first time, taking account of the thesis supervision commitment referred to in the admission decision.

The thesis supervisor will ensure that training activities carried out by the doctoral candidate are coherent and suitable, and that the topic of the candidate’s doctoral thesis will have an impact and make a novel contribution to knowledge in the relevant field. The thesis supervisor will also guide the doctoral candidate in planning the thesis and, if necessary, tailoring it to any other projects or activities undertaken. The thesis supervisor will generally be a UPC professor or researcher who holds a doctoral degree and has documented research experience. This includes PhD-holding staff at associated schools (as determined by the Governing Council) and UPC-affiliated research institutes (in accordance with corresponding collaboration and affiliation agreements). When thesis supervisors are UPC staff members, they also act as the doctoral candidate’s tutor.

PhD holders who do not meet these criteria (as a result of their contractual relationship or the nature of the institution to which they are attached) must be approved by the UPC Doctoral School's Standing Committee in order to participate in a doctoral programme as researchers with documented research experience.

The academic committee of the doctoral programme may approve the appointment of a PhD-holding expert who is not a UPC staff member as a candidate’s thesis supervisor. In such cases, the prior authorisation of the UPC Doctoral School's Standing Committee is required. A UPC staff member who holds a doctoral degree and has documented research experience must also be proposed to act as a co-supervisor, or as the doctoral candidate’s tutor if one has not been assigned.

A thesis supervisor may step down from this role if there are justified reasons (recognised as valid by the committee) for doing so. If this occurs, the academic committee of the doctoral programme will assign the doctoral candidate a new thesis supervisor.

Provided there are justified reasons for doing so, and after hearing any relevant input from the doctoral candidate, the academic committee of the doctoral programme may assign a new thesis supervisor at any time during the period of doctoral study.

If there are academic reasons for doing so (an interdisciplinary topic, joint or international programmes, etc.) and the academic committee of the programme gives its approval, an additional thesis supervisor may be assigned. Supervisors and co-supervisors have the same responsibilities and academic recognition.

The maximum number of supervisors of a doctoral thesis is two: a supervisor and a co-supervisor.

For theses carried out under a cotutelle agreement or as part of an Industrial Doctorate, if necessary and if the agreement foresees it this maximum number of supervisors may not apply. This notwithstanding, the maximum number of supervisors belonging to the UPC is two.

More information at the PhD theses section

The maximum period of study for full-time doctoral studies is four years, counted from the date of first enrolment in the relevant programme until the date on which the doctoral thesis is deposited. The academic committee of the doctoral programme may authorise a doctoral candidate to pursue doctoral studies on a part-time basis. In this case, the maximum period of study is seven years from the date of first enrolment in the programme until the date on which the doctoral thesis is deposited. To calculate these periods, the date of deposit is considered to be the date on which the thesis is made publicly available for review.

If a doctoral candidate has not applied to deposit their thesis before the expiry of the deadlines specified in the previous section, the academic committee of the doctoral programme may, at the request of the doctoral candidate, authorise an extension of this deadline of one year under the conditions specified for the doctoral programme in question.

Learning resources

The main resources used to carry out doctoral work are provided by the units responsible for the programme and participating units. They include very well-equipped laboratories with excellent technical equipment, highly specialised libraries, and spaces equipped to facilitate study and work by doctoral students during the completion of their thesis. Websites of the units responsible for the programme and participating units:

• Department of Automatic Control (ESAII) (https://esaii.upc.edu/es)
• Institute of Industrial and Control Engineering (IOC) (https://ioc.upc.edu/es)
• Institute of Robotics and Industrial Informatics (IRI)(https://www.iri.upc.edu/)

Doctoral Theses

List of authorized thesis for defense.

• SHEN, JIANXIONG: Incorporating Uncertainty into Neural Rendering for Interpretable 3D Modeling Author: SHEN, JIANXIONG Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis) Programme: DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION Department: Institute of Robotics and Industrial Informatics (IRI) Mode: Normal Deposit date: 21/06/2024 Reading date: pending Reading time: pending Reading place: pending Thesis director: MORENO NOGUER, FRANCESC D'ASSIS | RUIZ OVEJERO, ADRIÀ Committee:      PRESIDENT: HARO ORTEGA, GLORIA      SECRETARI: SÁNCHEZ RIERA, JORDI      VOCAL: PORZI, LORENZO Thesis abstract: 3D scene modeling refers to the process of creating a digital representation of real-world environments for better understandingand further manipulation. Early works involved the utilization of pure structured representations such as meshes or voxel grids.The recent success of deep learning has enabled implicit representations of the scenes using deep neural networks, whichtypically are of high efficiency in automatically learning from data and handling high-resolution complex scenes. However, theseimplicit representations are often not explainable with an automatic feature learning process, hindering their further applicationsin practical scenarios. For example, to reduce potentially catastrophic failures in high-risky fields such as healthcare orautonomous driving, the uncertainty associated with the modeling results must be included into the decision-making process.Aiming at more interpretable 3D scene modeling, this thesis explores various methods to quantify the uncertainty in the processof 3D scene modeling that utilizes implicit neural representation. Firstly, we propose stochastic neural radiance fields to model aprobabilistic framework for capturing the uncertainty on rendered RGB images and estimated depth from the learned scenemodel, with a drastically reduction of model complexity compared to previous Bayesianbased probabilistic methods. To enhanceits capability of handling more complex scenes with varying geometry and appearance, we next extend the modelexpressiveness by incorporating a flow-based generative models to automatically learn arbitrarily complicated densitydistributions in a flexible manner. While these methods are able to achieve accurate 3D modeling with reliable uncertaintyestimation, they still suffer from the time-consuming optimization process, impeding their further applications in practicalscenarios. Therefore, we then explore a hybrid representation with incorporated voxel grids to explicitly learn the volumetricdensity of each 3D position, dramatically improving the model efficiency in both optimization and inference. So far, the currentframework is inherently designed for estimating predictive uncertainty for the areas of the scene that can be observed in thetraining images, and fail to output reliable uncertainty involving unobserved scene contents such as occlusion for roboticexploration in unknown experiments. For this purpose, we finally propose to introduce a 3D uncertainty field based on the trainedmodel, exhibiting consistently high uncertainty for predictions from those unobserved scene regions.In summary, we design an efficient and reliable probabilistic framework for 3D scene modeling to quantify the comprehensiveuncertainty associated with the predictions both from observed and unobserved scene regions. This framework provides a solidtool for uncertainty-aware decision-making and analysis in a variety of applications that rely on 3D scene modeling.
• SUÁREZ HERNÁNDEZ, ALEJANDRO: New methods for bridging symbolic-geometric reasoning, addressing uncertainty and action learning in task planning for robotics Author: SUÁREZ HERNÁNDEZ, ALEJANDRO Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis) Programme: DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION Department: Institute of Robotics and Industrial Informatics (IRI) Mode: Normal Deposit date: 21/06/2024 Reading date: 25/07/2024 Reading time: 11:30 Reading place: Sala d'Actes de la Facultat de Matemàtiques i Estadística (FME), Campus Sud, Carrer de Pau Gargallo, 14, 08028 Barcelona Thesis director: TORRAS GENIS, CARMEN | ALENYÀ RIBAS, GUILLEM Committee:      PRESIDENT: ANGULO BAHON, CECILIO      SECRETARI: MORENO RIBAS, ANTONIO      VOCAL: UGUR, EMRE Thesis abstract: The physical world exhibits a wide range of obstacles to the application of robotics in a large number of tasks. Scripted behavior and/or teleoperated programs are still used in many industries (e.g. car assembly) because they are robust and reliable. However, their scope is limited and falls short in less controlled environments. We explore the possibilities of task planning, or Artificial Intelligence (AI) planning, for solving tasks in a more flexible, non-scripted way. In contrast to motion planning, AI planning focuses on high-level decision-making, rather than on concerns such as computation of trajectories and dynamics. Task planning is a very powerful tool for virtual situated agents (e.g. videogames or web services). One of its main advantages is that it allows deliberative, explainable, and adaptive behavior as long as a reliable model of the environment is available. However, the physical world presents some challenges that make the application of AI planning more difficult. This thesis has the following objectives, each one tied to a different challenge: (O1) integration of AI and motion planning; (O2) handling the unintended effects of actions taken by the robot; (O3) performing tasks even when the robot is not aware of all the relevant information; and (O4) automatic learning of action models to avoid the need for handcrafted ones. Our first contributions revolve mainly around objectives O1 to O3, which involve planning and acting. We propose hierarchical paradigms of planning, exploitation of topological properties of a problem for simplifying Markov Decision Processes, and planning alongside physical simulators to minimize the risk of unintended effects. The second part of our contributions focuses on O4, and consists of different algorithms for learning and recognizing STRIPS action schemata. Published results and findings are provided to support each contribution, alongside examples from manipulation scenarios, such as automatic disassembly of electromechanical devices, and socially assistive interactions.

Last update: 09/07/2024 04:45:23.

List of lodged theses

• FLORES VÁZQUEZ, CARLOS ALBERTO: CeCi: Design, Development and Validation of an Affordable Consumer Service Robot as a Social Robot Author: FLORES VÁZQUEZ, CARLOS ALBERTO Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis) Programme: DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION Department: Department of Automatic Control (ESAII) Mode: Normal Deposit date: 02/07/2024 Deposit END date: 15/07/2024 Thesis director: ANGULO BAHON, CECILIO Committee:      PRESIDENT: VALLÈS PERIS, NÚRIA      SECRETARI: REPISO POLO, ELY      VOCAL: TREJO RAMÍREZ, KARLA ANDREA Thesis abstract: This research discusses elements to be considered for designing, developing, and validating a service robot that performs its task in different social environments. Due to the social focus of the provided services, technical considerations are demanded toaccomplish the task, and the acceptability of use for the people interacting with the robot.The first stage of the research considers previous cases on the implementation of service mobile robots, their analysis, and the motivation of how to solve their acceptability and use by people. The developmental part presents the technical and social considerations for implementing the CeCi (Computer Electronic Communication Interface) social robot. Two main problems of social robots and service robots in social environments currently on the market are addressed, which are the main focus of this research: First, their costs are not affordable for many companies, universities, or individuals in developing countries. The second is that their design is exclusively oriented to the functional part with a viewpoint inherent to the engineers who create them without considering the end users’ views, preferences, or requirements, especially for their social interaction. This last reason ends up causing a certain aversion to the use of this type of robot.In response to the issues raised, an affordable, low-cost prototype is proposed, starting from a commercial platform for research development and using open-source code. The robot design presented here is centered on the criteria and preferences of the end user, prioritizing acceptability for social interaction. This document details the selection process and hardware capabilities of the robot. Moreover, a programming section is provided to introduce the different software packages used and adapted for social interaction, the main functions implemented, as well as the new and original part of the proposal. A list of applications currently developed with the robot and possible applications for future research are discussed.As a final step, the complete implementation of the social robot is explained based on two design elements. The first element is the use of the design thinking methodology for the development and implementation of the robot. The second element, of a technical nature, is a previous taxonomy generated for defining socially-aware robot assistants. Therefore, in the construction process, special emphasis is placed on the realization of prototypes and their adjustment to the users’ preferences. Interviewswith users who were unaware of the robot’s capabilities were used to improve and validate the prototype. These prototypes and their evolution will be presented based on the adjustments performed. Beyond the users’ feedback, previous experiences exposed to the state of the art were considered for the evolution of this robot. The entire methodological process is validated with surveys, and results are presented as a SWOT (strengths, weaknesses, opportunities, and threats) analysis for future improvements.
• WANG, CHUANSHENG: Research on signal and image denoising techniques Author: WANG, CHUANSHENG Thesis file: (contact the Doctoral School to confirm you have a valid doctoral degree and to get the link to the thesis) Programme: DOCTORAL DEGREE IN AUTOMATIC CONTROL, ROBOTICS AND VISION Department: Department of Automatic Control (ESAII) Mode: Normal Deposit date: 03/07/2024 Deposit END date: 16/07/2024 Thesis director: GRAU SALDES, ANTONI | GUERRA PARADAS, EDMUNDO Committee:      PRESIDENT: PALACÍN ROCA, JORGE      SECRETARI: SANFELIU CORTES, ALBERTO      VOCAL: RIBAS XIRGO, LLUÍS Thesis abstract: This dissertation aims to investigate two crucial tasks in the field of signal and image processing: signal denoising and image enhancement. Firstly, for signal processing, we propose three innovative denoising algorithms tailored specifically for one-dimensional EEG signals. These algorithms combine the strengths of deep learning and traditional signal processing techniques to effectively adapt to various noise types associated with different cognitive tasks, thereby enhancing the quality and accuracy of the signals. Secondly, in the domain of image processing, we introduce three novel image enhancement algorithms designed to tackle multiple noise types in natural scenes. By integrating deep learning methodologies with prior knowledge, these algorithms enhance image sharpness, contrast, and detail reproduction, demonstrating adaptability and reliability across different lighting, weather conditions, and photographic equipment. Lastly, we conduct a comprehensive analysis of the similarities and differences between image enhancement and signal denoising tasks. Through comparing the methodologies employed by each in handling diverse noise types, we derive meaningful conclusions to guide future research. These contributions are poised to significantly advance technological capabilities and theoretical understanding in both domains.

Last update: 09/07/2024 04:30:28.

List of defended theses by year

Select a year: 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024

Committee:      PRESIDENT: IVANOVA RADEVA, PETIA      SECRETARI: BENITEZ IGLESIAS, RAUL      VOCAL: ORTEGA RAMÍREZ, JUAN ANTONIO Thesis abstract: Clinical pathologists focus on detailed morphological analysis of peripheral blood (PB) cells for the recognition of abnormal cells and contribute to the diagnosis of hematological diseases. While this traditional method is often precise, it requires significant expertise, is time-consuming, and is subject to variability, especially when discerning subtle cell characteristics. In light of these challenges, this dissertation proposes the innovative use of deep learning, specifically convolutional neural networks (CNNs) and generative adversarial networks (GANs), to add objectivity in morphological examination of PB cells. The research is developed through a three-fold strategy. First, a novel GAN-based model standardizes PB cell image visualization across varied staining techniques. Second, optimized CNN architectures are tailored for precise classification of the selected PB cell types, with emphasis on reactive and malignant blood cells. Results indicate that the introduced Stain Normalization Model effectively manages staining variations while preserving cell morphology. Furthermore, CNN models, based on these normalized data, contribute significantly to the automatic recognition of malignant cells. And third, a modular GAN system generates synthetic PB images to enrich model training, particularly of low-prevalence cell data with special diagnostic interest.This research emphasizes the transformative impact of combining the expertise of clinical pathologists with advanced computational strategies. While these systems serve as potent support tools, it is imperative to note that the clinical pathologist retains the ultimate authority in decision-making. The fusion of traditional expertise and modern technology is a promising trend in the present and near future, enhancing both objectivity and efficiency. This study signifies an advance towards the establishment of an integrated system for an automated screening tool for hematological diseases.

Committee:      PRESIDENT: MANSARD, NICOLAS      SECRETARI: MORCEGO SEIX, BERNARDO      VOCAL: LIPPIELLO, VINCENZO Thesis abstract: Aerial manipulators, which commonly take the form of multirotors with attached robotic limbs, primarily employ their limbs for pure manipulation tasks and do not rely on them during aerial locomotion. Besides, their movement tends to be slow. This thesis aims to enhance an aerial manipulator’s agility by harnessing its limb’s capabilities to augment its overall motion. This objective involves investigating various modes of utilizing the limb: as a tail for aerial locomotion, as an arm for aerial manipulation, or as a leg for hybrid aerial-contact locomotion. The present thesis contributes to two specific domains: 1. Generation and control of agile motions for aerial manipulators, 2. Design and construction of a specialized aerial manipulator for executing agile motions.Generating agile motions requires predicting the movement of the robot considering its dynamics so that these dynamics can be used to favor the robot’s motion. Hence, we can achieve complex maneuvers with relative ease. Optimal control is a trajectory-generation technique that meets these requirements, and that is central to this thesis. We encode the robot’s tasks as cost functions of the optimal control problem (OCP) and use the whole-body dynamics as the constraints of the dynamic system. On the control side, to deploy such trajectories in a real robot, we use model predictive control (MPC) techniques, which is the closed-loop control extension of optimal control. To get the control command, an MPC controller solves the OCP in which we have encoded the agile trajectory, and then the controller applies the first command of the solution control trajectory. Thus, MPC requires solving an OCP at the control rate, i.e., within a few milliseconds. This forces us to use fast, specialized solvers based on the dynamic programming principle, such as differential dynamic programming (DDP). In their original form, these solvers cannot consider the control bounds. These bounds are important to create trajectories compatible with the real robot. To tackle this problem, in this thesis, we propose two DDP-based methods to consider the control bounds: one is based on a squashing function, and the other is based on a projection method. Even with these solvers, we face challenges in meeting the solving rate and are forced to reduce the MPC horizon. Reducing the MPC horizon implies that the MPC can only see a portion of the original OCP, possibly leaving out some of the tasks. This affects the predictive capability of the controller and compromises the accomplishment of the tasks, especially those that require an elaborate and dynamic maneuver. To overcome this difficulty, in this thesis, we propose to update, at each MPC iteration, the terminal cost function in the MPC with a function that encodes the part of the trajectory that remains unseen by the controller.Regarding robot design, deploying agile motions becomes difficult with existing aerial manipulators, which are generally big-size multirotor platforms with non-compliant, high-gear ratio limbs. In this thesis, we present Borinot, an open-source aerial robotic platform designed to research hybrid agile locomotion and manipulation using flight and contacts. This platform features an agile and powerful hexarotor that can be outfitted with torque-actuated limbs of diverse architecture, allowing for whole-body dynamic control. We present experiments with this robot showcasing different agile motions.In addition to the stated contributions, this thesis contributes in other areas required to operate the robot, such as a procedure for identifying the dynamical parameters based on factor-graph estimation or a hardware enhancement that allows for direct thrust control of Borinot’s rotors.

Committee:      PRESIDENT: DE SCHUTTER, BART HENRI      SECRETARI: GRIÑO CUBERO, ROBERTO      VOCAL: OLARU, SORIN Thesis abstract: This doctoral thesis explores the utilization of generalized Nash equilibrium (GNE) seeking methods for evolutionary games over networks in designing optimization-based controllers for large-scale complex systems (LSCSs). The focus lies on population games, modeling strategic interactions among large populations of agents with bounded rationality. These games are deemed suitable for modeling large-scale optimization-based control problems either directly or by employing evolutionary dynamics models (EDMs) to update optimization variables. The concept of GNE emerges as a fitting solution for non-cooperative optimization-based control scenarios, where multiple subsystems maximize their utilities subject to coupling constraints. Consequently, this thesis frames optimization-based control as a GNE seeking problem and introduces novel game-theoretical methods for achieving GNEs in population games over networks. Applications in congestion games, charging coordination of plug-in electric vehicles (PEVs), dynamic resource allocation in solar collector fields, and residential demand response are discussed.The first part focuses on GNE seeking methods for population games under full information schemes, where non-local information estimation is not explicitly modeled. Two approaches are proposed: one dealing with capacity-constrained population games, introducing novel strategy revision protocols to ensure capacity constraint satisfaction, and another addressing population games with affine equality and convex inequality constraints, devising a payoff dynamics model (PDM) steering agents towards GNEs. Formal asymptotic stability guarantees are provided, and these methods are applied to design controllers for dynamic resource allocation in LSCSs.In contrast, the second part formulates GNE seeking methods for population games under partial information schemes, explicitly modeling non-local decision information estimation. It tackles reaching Nash equilibria without coupling constraints and achieving GNEs with affine equality constraints coupling multiple agents' decisions. Novel PDMs are formulated to estimate non-local information and compute dual Lagrange multipliers associated with coupling constraints. Asymptotic stability guarantees are provided, and the methods are applied to large-scale congestion games.The third part introduces hybrid PDMs combining continuous and discrete-time dynamics. Two event-triggered PDMs are devised to accommodate practical control features like logic implications and occasional communication. One mechanism focuses on PEV charging coordination, considering charging completion and random arrivals/departures of PEVs, while another aims to reduce communication costs in residential demand response. Formal convergence guarantees towards GNEs are deduced for both cases.In summary, this thesis contributes theoretically and practically to GNE seeking methods for population games over networks and to the design of optimization-based control strategies for LSCSs.

Committee:      PRESIDENT: VALIÑO GARCÍA, LUIS      SECRETARI: COSTA CASTELLO, RAMON      VOCAL: MORÉ, JERÓNIMO JOSÉ Thesis abstract: Fossil fuels have played a pivotal role in driving global economic development. However, their rampant use has brought about severe environmental consequences, particularly air pollution and greenhouse gases (GHG) emmissions, which poses substantial threats to human health and the planet's well-being. To address these pressing issues, the development of efficient and sustainable technologies like Proton Exchange Membrane Fuel Cells (PEMFCs), powered by hydrogen from renewable sources, has become paramount. PEMFCs hold immense promise as a viable solution for decarbonization and mitigating air pollution and GHG emissions associated with transportation and vehicles. Nvertheless, overcoming challenges related to their efficiency and longevity remains a crucial step forward.Specifically, this thesis explores the application of PEMFCs in an automotive fuel cell electric vehicle. The longevity and durability of a fuel cell system, together with its efficiency, are pivotal factors in the advancement of fuel cell vehicles, as they significantly impact the cost and consumer acceptance of these vehicles. In the domain of automotive applications, the primary contributors to fuel cell degradation include challenging operating conditions (involving temperature, humidity, pressure, mass flow rate, high and low power densities), dynamic load profiles, improper handling during start-stop and idle modes, and the presence of contaminants in the reactant gases. A distributed-parameter model of the stack is presented in this thesis, accurately depicting the internal dynamic behavior of the PEMFC. Additionally, the models of balance-of-plant subsystems, including anode, cathode, and thermal subsystems are introduced and implemented in Simulink. All these models are experimentally validated. Based on these models, a model predictive control strategy is designed to obtain the optimal operating conditions for the stack and generate from these the balance-of-plant subsystem setpoints.A supervisory controller for a fuel cell system is designed. This supervisory controller comprises three key components: a setpoint generator, a state machine, and a power limit calculator. The supervisory controller is subjected to rigorous simulation testing using a standard automotive driving cycle.These sophisticated control methodologies and algorithms are then applied to the automotive system developed in the European INN-BALANCE project, which served as the foundation for this PhD Thesis. Therefore, this thesis presents experimental results obtained from the implementation of the proposed controller in a prototype vehicle at PCS and CEVT facilities, encompassing startup, shutdown, dynamic load tracking, and subsystem local controller performance.Finally, this thesis provide a comprehensive overview of the contributions made by the research and its outcomes. The conclusion also proposes potential avenues for future research, marking the culmination of the investigative journey undertaken in this thesis.

Committee:      PRESIDENT: SERRATOSA CASANELLES, FRANCESC ASSIS      SECRETARI: GRAU SALDES, ANTONI      VOCAL: VENTURA ROYO, CARLES Thesis abstract: Surface inspection of coated surfaces in the automotive industry, traditionally has been a manual process in charge of keen eye operators in charge of inspecting the whole car body. However, as might be deducted, manual inspections often lack repetitiveness and reliability, very much desired in a such a strict sector. Computer vision tackled this problem with the first automated defect detection systems, pinpointing the defects in the car body and their size. Nevertheless, as these systems are constrained to just detection, the operator is still in charge of properly labelling the defects to rework them correctly. Additionally, there is a lack of traceability between the process and the defects themselves, taking longer to identify the root causes of faulty paint shop facilities.In this thesis, we address the multidisciplinary problem of defect identification in specular surfaces, with two main research lines. In the first one, we developed a novel illumination approach based on indirect diffuse lighting, in contrast with the conventional specular reflection. Together with a high resolution camera, we demonstrated an important improvement in terms of defect recognition with respect to the existing defect detection systems. These results are assessed with specialized auditors from the SEAT Martorell factory.The second research line, oriented to computer vision, explore the possibilities of implementing a deep learning solution for industrial defect identification. We developed a fast and reliable context aggregation model, featuring dilated convolutions and residual connections between opposite layers. This model is then trained following a loss leverage between classification and segmentation, for a smoother training procedure. Additionally, in order to cope the frequent class imbalances in the industrial datasets, we developed a guided-crop image augmentation strategy, based on cropping real defect randomly into non-defective images to generate synthetic new samples. The results state that the combination of this model with this augmentation strategy is able to outperform well-known segmentation models.Eventually, for data scarcity situations, we resorted to image synthesis methods to generate new fake samples. Models like Pix2pix have proven to be able to generate close to real im- ages, helping the segmentation model to converge faster than with the previous guided-crop image augmentation technique. Later, this generative method will be surpassed by a more sophisticated one, which features spatially-adaptive normalization layers that help to synthe- size images even without an encoder. Overall, it demonstrated good capabilities in multiple industrial datasets.

Committee:      PRESIDENT: NGUYEN, ANH-TU      SECRETARI: BLESA IZQUIERDO, JOAQUIN      VOCAL: IFQIR, SARA Thesis abstract: Autonomous driving is a promising solution for current transportation systems that would provide sustainable traffic flow, efficient mobility, and pollution reduction. However, the interaction of multiple vehicles presents several engineering challenges, such as vehicle-to-vehicle communication and critical decision-making. Robustness is another challenge that is crucial to ensure the proper design of autonomous driving technology. Vehicles are subject to significant uncertainty due to modeling errors, discretization of sensor signals, or external signals that adversely affect their nominal behaviour. This thesis proposes robust and optimal model-based approaches to address motion planning and control problems. Robustness is addressed from a set theory perspective. Specifically, the advantages of zonotopic sets are used to exploit the design of tube constraints in planning and control, as well as to quantify and propagate uncertainty in the modeling and estimation phases. In the field of modeling, this thesis utilizes control/planning-oriented models using the Takagi-Sugeno (TS) and linear parameter-varying (LPV) paradigms to represent the non-linear model of the vehicle. These representations are obtained from the physical equations of motion or directly from data. The data-driven dynamic model is trained using an adaptive neuro-fuzzy inference system (ANFIS). Meanwhile, the uncertainty associated with the model parameters is estimated through a zonotopic recursive least-square (ZRLS) approach.In the field of motion control, this thesis proposes a two-layer trajectory tracking robust strategy based on model predictive control (MPC) in the outer layer. In the inner layer, a combination of a linear quadratic zonotopic controller (LQZ) and a zonotopic Kalman filter (ZKF) is used. The vehicle's energy is managed by the MPC through the state of charge of its battery while ensuring performance indexes in terms of longitudinal, lateral, and angular velocities. To achieve this, the strategy combines a data-driven dynamic model with kinematic and state-of-charge models.Subsequently, the two-layer robust configuration is utilized for planning purposes. In the first step, the model predictive planner (MPP) generates feasible references online while maximizing both the longitudinal velocity and the saving energy through the battery state of charge. This is different to the control stage, in which the controller follows the reference previously computed offline. Additionally, the planner integrates a data-driven prediction model and a computationally efficient driving-corridor-like method based on track width constraints to handle collision avoidance for both static and moving obstacles. In the second step, an MPP is proposed to address coordination between connected autonomous vehicles. This planner utilizes the LPV representation of the non-linear vehicle model, which is derived from the physical equations of motion using the non-linear embedding approach. Furthermore, it incorporates mixed-integer linear inequalities to ensure safe overtaking maneuvers and avoid non-linear constraints imposed by obstacle boundaries. To assess the effectiveness of the proposed control and planning approaches, a small 1/10 scale electric car is utilized. Furthermore, simulations are performed in aggressive autonomous driving regimes, which involve steep curves and driving at maximum velocity.

Last update: 09/07/2024 05:00:50.

Theses related publications

Research projects

Teaching staff and research groups

Research groups.

UPC groups:

• ACES-Advanced Control of Energy Systems
• CoDAlab-Control, Dynamics and Applications
• CS2AC-UPC-Supervision, Safety and Automatic Control
• GREC-Knowledge Engineering Research Group
• GRINS-Intelligent Robots and Systems
• ROBiri-IRI Robotics Group
• SAC-Advanced Control Systems
• SCOM-Supply Chain and Operations Management
• SEPIC-Power and Control Electronics Systems
• SIC-Smart Control Systems
• SIR-Service and Industrial Robotics
• VIS-Artificial Vision and Intelligent Systems

Doctoral Programme teachers

• Alenyà Ribas, Guillem
• Alquezar Mancho, René
• Andrade Cetto, Juan
• Angulo Bahon, Cecilio
• Aranda Lopez, Joan
• Basañez Villaluenga, Luis
• Batlle Arnau, Carles
• Benitez Iglesias, Raul
• Biel Sole, Domingo
• Blesa Izquierdo, Joaquim
• Bolea Monte, Yolanda
• Cembrano Gennari, M.Gabriela Elena
• Climent Vilaro, Joan
• Costa Castello, Ramon
• Escobet Canal, Teresa
• Fossas Colet, Dr. E.
• Grau Saldes, Antoni
• Jimenez Schlegl, Pablo
• Marti Colom, Pau
• Morcego Seix, Bernardo
• Moreno Noguer, Francesc d'Assis
• Nejjari Akhi-Elarab, Fatiha
• Ocampo Martinez, Carlos
• Pastor Moreno, Rafael
• Peña Pitarch, Esteban
• Perez Magrane, Ramon
• Porta Pleite, Jose Maria
• Quevedo Casin, Joseba
• Rodellar Benede, Jose
• Ros Giralt, Lluis
• Ruiz Vegas, Francisco Javier
• Sarrate Estruch, Ramon
• Serra Prat, Maria
• Suarez Feijoo, Raul
• Thomas Arroyo, Federico
• Tornil Sin, Sebastian
• Torras Genis, Carme
• Velasco Garcia, Manel

Other teachers linked to the Doctoral Programme

• Agudo Martínez, Antonio
• Borràs Sol, Júlia
• Colomé Figueras, Adria
• Cuguero Escofet, Josep
• Delgado Prieto, Miquel
• Diaz Boladeras, Marta
• Dimiccoli, Maria
• Fernandez Ruzafa, Josep
• Foix Salmeron, Sergi
• Garrell Zulueta, Anais
• Guerra Paradas, Edmundo
• Guzman Sola, Ramon
• Hernansanz Prats, Alberto
• Husar, Attila
• Jevtic, Aleksandar
• Nadeu Camprubi, Climent
• Ponsa Asensio, Pere
• Sánchez Riera, Jordi
• Santamaria Navarro, Angel
• Sola Ortega, Joan
• Vidal Segui, Yolanda

External teachers

CASAS GUIX, MARC ( Barcelona Supercomputing Center (BSC) )

DE PRADA GIL, MIKEL ( Institut de Recerca en Energia de Catalunya (IREC) )

DUVIELLA, ERIC ( IMT Lille Douai )

LÓPEZ ESTRADA, FRANCISCO RONAY ( Instituto Tecnológico de Tuxtla Gutiérrez )

MIRATS TUR, JOSEP M. ( Inloc Robotics )

NA, JING ( University of Bristol )

SALOM TORMO, JAUME (Institut de Recerca en Energia de Catalunya (IREC))

SANCHEZ PEÑA, RICARDO ( Instituto Tecnológico de Buenos Aires, Argentina )

The Validation, Monitoring, Modification and Accreditation Framework (VSMA Framework) for official degrees ties the quality assurance processes (validation, monitoring, modification and accreditation) carried out over the lifetime of a course to two objectives—the goal of establishing coherent links between these processes, and that of achieving greater efficiency in their management—all with the overarching aim of improving programmes.

• Verification Memory (Doctoral Programme) - 2013
• Verification Resolution (MECD)
• Agreement of the Council of Ministers (BOE)
• Monitoring report (Doctoral Programme) - 2018
• University monitoring report (Doctoral School) - 2018

Accreditation

• Official Degree Accreditation Evaluation Report (AQU) - 2021

Registry of Universities, Centers and Degrees (RUCT)

• Registration of the Doctoral Programme in the RUCT

• Artificial Being (ABe)
• Synthetic Emotions
• Mobile Agents
• Tartarus: Multi-Agent Platform for IoT/CPS
• Typhon: Programming Mobile Agents for Real World Emulation
• Immune Inspirations: Emulating the Idiotypic Network
• Speech Analysis
• Speech Recognition
• Speaker Characterization
• Accent Analysis
• Speech Diarization
• Our Alumni – Our Accolades!
• Bio-Inspired, Networked & Emotional Robotics
• Speech – Analysis, Recognition & Categorization
• Shivashankar B. Nair
• Pradip Kr. Das
• NI LabVIEW Robotics Starter Kit
• Lego® Mindstorm™ NXT
• Indian Institute of Technology Kanpur Robotic Platform
• Speech Related
• Software – Robotics
• Software – Speech
• Other Hardware/Software
• Collaborations
• CFPs & Interesting Links
• Achievements & Happenings
• Tartarus: A Multi-Agent Emulator (Open Source)
• Previous in-House Technology Downloads

Publications & Doctoral Theses (Bio-Inspired, Networked & Emotional Robotics)

The links to Ph.D. theses of students together with some of the major publications  are available below in chronological order are listed below. For an area-wise list of the same click HERE – this may however not be up-to-date.

Theses/Papers tagged [Typhon] indicate that the reported work used Typhon – A Mobile Agent Emulation Platform, developed at this lab., for realization while those tagged [ Tartarus ] indicate that the same used the new SWI-Prolog based multi-threaded version of Typhon viz . Tartarus . Theses/Papers tagged [ AgPi ] indicate that the Raspberry Pi version of Tartarus was used while those tagged [ TarPy ] used the Python version of Tartarus . Those interested in using Tartarus may visit the link:  https://github.com/roboticslab-cseiitg/ProjectTartarus

Ph.D. Thesis/Student Links :

2023 : Divya Kulkarni:  Immuno-inspired Lifelong Learning in Robots [Tartarus/TarPy]  {Divya Kulkarni is currently a Machine Learning Scientist at Eli Lilly, Bengaluru}

2019 : Sonia: Non-intrusive human sensing-techniques and applications {Sonia is currently a Machine Learning Scientist at Eli Lilly, Bengaluru}

2019 :  Tushar Semwal : On Decentralizing Intelligence in Cyber-Physical Systems [Tartarus] [AgPi]  {Tushar Semwal is currently an Applied Scientist-II at Microsoft, Hyderabad}

2016 : Shashi S. Jha : On Mobile Agents for Learning & Coordination in a Networked Robotics Milieu [ Typhon ] {Shashi S. Jha is an Assistant Professor at the Dept. of Computer Science & Engg., Indian Institute of Technology Ropar}

2016: Samir Kumar Borgohain :  An Immuno-inspired Technique for Language Generation & a Pictorially Grounded Language for Machine Aided Translation {Samir Borgohain is an Associate Professor at the Dept. of Computer Science & Engg., National Institute of Technology Silchar}

2011 : Wilfred W. Godfrey : Mobile Agent Based bio-Inspired Mechanisms for Servicing Networked Robots {Wilfred W. Godfrey is an Associate Professor at the Dept. of Computer Science & Engg., ABV Indian Institute of Information Technology & Management Gwalior}

Publications

• Gayathri Rangu and Shivashankar B. Nair, Immuno-inspired Selective Aggregation for Decentralized Federated Deep Reinforcement Learning ,  Genetic and Evolutionary Computation Conference, GECCO  2024 14-18th July 2024, Melbourne, Australia [CORE Ranking A]   (Accepted)
• Suraj Pandey and Shivashankar B. Nair, Onboard Class Incremental Learning for Resource-Constrained Scenarios using Genetic Algorithm and TinyML , Genetic and Evolutionary Computation Conference, GECCO  2024, 14-18th July 2024, Melbourne, Australia [CORE Ranking A]   (Accepted)
• Gayathri Rangu and Shivashankar B. Nair. 2023. On Mobile-Agent-based Swarm Reinforcement Learning in a Heterogeneous Robotic Network . In Proceedings of the 2023 6th International Conference on Advances in Robotics (AIR ’23). Association for Computing Machinery, New York, NY, USA, Article 80, 1–6. https://doi.org/10.1145/3610419.3610499 [TarPy]
• Divya D. K., Nair, S.B., Transfer Learning for Embodied Neuroevolution , Workshop on Neuroevolution at work, Genetic and Evolutionary Computation Conference, GECCO 2023 , 15-19th July 2023, Lisbon, Portugal [CORE Ranking A]   (Accepted)
• Pandey, S.K., Nair, S.B., Enhancing Siamese Neural Networks for Multi-class Classification: An Immuno-inspired Approach , Hot Off  the Press Track, Genetic and Evolutionary Computation Conference, GECCO 2023, 15-19th July 2023 , Lisbon, Portugal [CORE Ranking A]   (Accepted)
• Rangu, G., Kulkarni, D.D., Nair, J.S., Nair, S.B. (2024). A Hybrid Federated Reinforcement Learning Approach for Networked Robots . In: Swain, B.P., Dixit, U.S. (eds) Recent Advances in Electrical and Electronic Engineering. ICSTE 2023. Lecture Notes in Electrical Engineering, vol 1071. Springer, Singapore, https://doi.org/10.1007/978-981-99-4713-3_47 [TarPy]
• J. S. Nair, D. D. Kulkarni, A. Joshi and S. Suresh, On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios , 2022 7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), Ioannina, Greece, 2022, pp. 1-8, doi: 10.1109/SEEDA-CECNSM57760.2022.9932985. [Tartarus]
• M. J. Bagchi, D. D. Kulkarni, S. B. Nair and P. K. Das,   On Embedding a Dataflow Architecture in a Multi-Robot System , 2022 Sixth IEEE International Conference on Robotic Computing (IRC), Italy, 2022, pp. 271-276, doi: 10.1109/IRC55401.2022.00052.  [Tartarus]
• Pandey, S.K., Nair, S.B. (2023), Immuno-Inspired Augmentation of Siamese Neural Network for Multi-class Classification , In: Yan, W.Q., Nguyen, M., Stommel, M. (eds) Image and Vision Computing. IVCNZ 2022. Lecture Notes in Computer Science, vol 13836. Springer, Cham. https://doi.org/10.1007/978-3-031-25825-1_35
• Jayprakash S. Nair, Divya D. Kulkarni, Ajitem Joshi, Sruthy Suresh, On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios ,  7th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM), 2022, pp. 1-8, doi: 10.1109/SEEDA-CECNSM57760.2022.9932985. [Tartarus]
• Divya D. Kulkarni, Tushar Semwal and Shivashankar B. Nair, Immuno-Inspired Management of Halls of Fame for Embodied Evolution , Swarm and Evolutionary Computation,  Elsevier, Volume 70, 2022,101054
• Best Student Paper : Divya Kulkarni and Shivashankar B. Nair, An Immuno-Inspired Transfer Learning Paradigm , IEEE Congress on Evolutionary Computation (CEC), 28.06-1.07.2021, Kraków, Poland [CORE Ranking B]
• Suraj Kr. Pandey, Sonia, Tushar Semwal, Shivashankar B. Nair, Smart Patch: An IoT Based anti Child Trafficking Solution , IEEE International Conference on Internet of Things & Intelligence System , (IoTIS), 27-28th January 2021, Bali, Indonesia
• Akul Agrawal, Divya Kulkarni and Shivashankar B. Nair, On Decentralizing Federated Learning , IEEE SMC International Conference on Systems, Man and Cybernetics, SMC-2020, Oct. 11-14th, Toronto, Canada  (Accepted)  [CORE Ranking B] [Tartarus]
• Divya Kulkarni and Shivashankar B. Nair,  Mutational Puissance Assisted Neuroevolution , GECCO ’20: Proceedings of the ACM-SIGEVO 2020 Genetic and Evolutionary Computation Conference Companion,  July 8th-12th 2020, Cancun, Mexico, pp. 1841-1848 {8 Pages}  [CORE Ranking A] https://doi.org/10.1145/3377929.3398149 [Tartarus]
• Tushar Semwal and Shivashankar B. Nair,  A Decentralized Artificial Immune System for Solution Selection in Cyber-Physical Systems , Applied Soft Computing, Elsevier Publ., Volume 86, January 2020, 105920, pp. 1-18, 2020. {18 pages} [Tartarus]
• Divya Kulkarni and Shivashankar B. Nair, Agrilogistics – A Genetic Programming Based Approach , EAI endorsed International Conference on Smart City for Life, (SCI4Life), Dec. 4th-6th 2019, Braga, Portugal,  In: Pereira P., Ribeiro R., Oliveira I., Novais P. (eds) Society with Future: Smart and Liveable Cities. SC4Life 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 318, pp.83-96, {14 Pages} Springer, Cham. [Tartarus]
• Tushar Semwal and Shivashankar B. Nair, An Immuno-inspired Distributed Artificial Classification System , 9th International Conference on Soft Computing for Problem Solving – SocProS 2019, (Unlocking the Power and Impact of Artificial Intelligence), Sept. 02nd-04th, 2019, Liverpool, UK, Nagar A., Deep K., Bansal J., Das K. (eds) Soft Computing for Problem Solving 2019. Advances in Intelligent Systems and Computing, vol 1138, pp. 31-49 {19 Pages}. Springer, Singapore. [Tartarus]
• Ishita, Divya Kulkarni, Tushar Semwal and Shivashankar B. Nair, On Securing Mobile Agents using Blockchain Technology , Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), Gangtok, India, 25-28th Feb. 2019, 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), pp. 1-5. {5 Pages} doi: 10.1109/ICACCP.2019.8882907 [Tartarus]
• Rahul Mishra, Tushar Semwal and Shivashankar B. Nair, A Distributed Epigenetic Shape Formation and Regeneration Algorithm for a Swarm of Robots , GECCO ’18: Proceedings of the Genetic and Evolutionary Computation Conference Companion, July 2018, pp. 1505–1512, {8 Pages} https://doi.org/10.1145/3205651.3208300 [CORE Ranking A]
• Tushar Semwal, Divya Kulkarni and Shivashankar B. Nair, On an Immuno-inspired Distributed, Embodied Action-Evolution cum Selection Algorithm , Genetic and Evolutionary Computation Conference (GECCO ’18, sponsored by ACM SIG on Genetic and Evolutionary Computation, SIGEVO), Kyoto, Japan, July 15-19th, 2018, Proceedings of the Genetic and Evolutionary Computation Conference July 2018 Pages 141–148 {8 Pages} https://doi.org/10.1145/3205455.3205584  [CORE Ranking A] [Tartarus] Video Link
• Tushar Semwal, Gaurav Mathur, Promod Yenigalla and Shivashankar B. Nair, A Practitioners’ Guide to Transfer Learning for Text Classi fication using Convolutional Neural Networks ,  SIAM International Conference on Data Mining (SDM 2018), San Diego, California, USA, Proceedings of the 2018 SIAM International Conference on Data Mining, pp. 513-521 {9 Pages}   [CORE Ranking A]
• Tushar Semwal and Shivashankar B. Nair, MAVNet: A Mobile Agent based framework for Vehicular Networks ,  Proceedings of the 3rd International Conference On Internet of Things: Smart Innovation and Usages (IoT-SIU-2018), Bhimtal, 2018, pp. 1-6, {8 Pages} doi: 10.1109/IoT-SIU.2018.8519932. [Tartarus]
• Shashi S. Jha, Shivashankar B. Nair, TANSA: Task Allocation using Nomadic Soft-Agents for Multi-Robot Systems ,  IEEE Transactions on Emerging Topics in Computational Intelligence, Vol. PP, Issue: 99, pp. 1-11, 2017. {11 Pages} [ Video ] [Typhon]
• Tushar Semwal, Shashi S. Jha, Shivashankar B. Nair, On Ordering Multi-Robot Task Executions within a Cyber Physical System ,  ACM Transactions on Autonomous and Adaptive Systems (TAAS) , Volume 12 Issue 4, Article No. 20, November 2017. {27 Pages}  [Video] [Tartarus]
• Sonia, Shivashankar B. Nair and Rashmi Dutta Baruah, An Immuno-inspired Online Feature Selection Mechanism , Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, SMC-2017, Banff, Canada (5-8th Oct. 2017), pp. 2123-2128, {6 Pages} doi:10.1109/SMC.2017.8122933.   [CORE Ranking B]
• Sonia, Rashmi Dutta Baruah and Shivashankar B Nair, A reward and penalty based approach for Online Feature Selection , 3rd IEEE International Conference on Cybernetics (CYBCON), 2017, Exeter, U.K. (21 – 23 June 2017) {6 Pages}
• Nitu Gangwar, Tushar Semwal, Shivashankar B. Nair, CARE: An IoT based System for Passenger Service and Comfort in Railways , COMSNETS 2017, 9th International Conference on COMmunication Systems & NETworkS, January 4-8th, 2017, Bengaluru, India, pp. 55-62 {8 Pages}. [AgPi]
• Sonia, Manish Singh, Rashmi Dutta Baruah and Shivashankar B Nair, A Voting-Based Sensor Fusion Approach for Human Presence Detection , In: Basu A., Das S., Horain P., Bhattacharya S. (eds) Intelligent Human Computer Interaction. IHCI 2016. Lecture Notes in Computer Science, vol 10127. Springer, Cham, pp. 195-206 { 12 Pages}
• Tushar Semwal, Shivashankar B. Nair, AgPi: Agents on Raspberry Pi , Special Issue of the Journal – Electronics: “Raspberry Pi Technology”, MDPI Open Access, (Click the paper title link to access the paper), {21 Pages}. [Tartarus] [AgPi]
• Nikhil S., Tushar Semwal and Shivashankar B. Nair, Immuno-Inspired Behaviour Adaptation in Multi-Robot Systems , IEEE International Conference on Systems, Man and Cybernetics, SMC-2016, Oct. 9-12th, Budapest, Hungary, pp.3199-3204  {Six Pages}. [Video] [CORE Ranking B]
• Tushar Semwal, Nikhil S., Shashi Shekhar Jha, Shivashankar B. Nair, TARTARUS: A Multi­ Agent Platform for Bridging the gap between Cyber and Physical Systems , Proceedings of the 2016 Autonomous Agents and Multi Agent Systems Conference, International Foundation for Autonomous Agents and Multiagent Systems, pp. 1493-1495, Singapore, (9-13th March 2016) {3 Pages}. [Video] [CORE Ranking A*] [Tartarus]
• Sonia,A. M. Tripathi,R. D. Baruah and Shivashankar B. Nair, Ultrasonic sensor-based human detector using one-class classifiers ,IEEE International Conference on. Evolving and Adaptive Intelligent Systems (EAIS 2015), (pp. 1-6).
• Shashi Shekhar Jha, Shivashankar B. Nair, An Idiotypic Solution Sieve for Selecting the Best Performing Solutions in Real-World Distributed Intelligence , IEEE Third International Conference on Artificial Intelligence, Modelling and Simulation, Kota Kinabalu, Sabah (Malaysia) 2 – 4 December 2015. pp. 71-76, 2015, {6 Pages}.  [Typhon]
• Shashi Shekhar Jha, Shivashankar B. Nair,  On a Multi-Agent Distributed Asynchronous Intelligence-Sharing and Learning Framework , LNCS Transactions on Computational Collective Intelligenc e XVIII , Vol. 9240, Chapter 9, pp.  166-200, 2015, Springer-Verlag Berlin Heidelberg, DOI:10.1007/978-3-662-48145-5_9, URL:http://dx.doi.org/10.1007/978-3-662-48145-5_9 {35 Pages} [Typhon]
• Mayank Gupta, Pulkit Verma, Tuhin Bhattacharya, Pradip K. Das, A Mobile Agents based Distributed Speech Recognition Engine for Controlling Multiple Robots , ADVANCES IN ROBOTICS – 2nd International Conference of Robotics Society of India, (ACM – In co-operation & Korea Robotics Society) 2nd to 4th July, 2015, Goa, India {6 Pages} [Typhon]
• Tushar Semwal, Manoj Bode, Vivek Singh,  Shashi Shekhar Jha, Shivashankar B. Nair, Tartarus: A Multi-Agent Platform for Integrating Cyber-Physical Systems and Robots , ADVANCES IN ROBOTICS – 2nd International Conference of Robotics Society of India, Proceedings of the 2015 Conference on Advances In Robotics (AIR ’15), ACM, New York, NY, USA, , Article 20 , {6 Pages}. DOI=http://dx.doi.org/10.1145/2783449.2783469  [Video] [Tartarus]
• Manoj Bode, Shashi S. Jha, Shivashankar B. Nair, On Movement of Emergency Services amidst Urban Traffic , Transactions on Ambient Systems , EAI Endorsed Transactions on Ambient Systems 11 – 12 2015 | Volume 2,  Issue 8, e4, Dec. 2015 {10 Pages} [Tartarus]
• Samir Borgohain, Shivashankar B. Nair, An Immuno-inspired approach towards Sentence Generation , Proceedings of the 2015 on Genetic and Evolutionary Computation Conference (GECCO ’15, sponsored by ACM SIG on Genetic and Evolutionary Computation, SIGEVO), Sara Silva (Ed.). ACM, New York, NY, USA, pp. 97-104, {8 Pages}. DOI=10.1145/2739480.2754805 (IEEE Conf. Listing Rank A )
• Book Chapter : Saurabh K. Singh, Shashi Shekhar Jha and Shivashankar B. Nair , On Realizing Emotional Memories , Handbook of Research on Synthesizing Human Emotion in Intelligent Systems and Robotics, Editors Jordi Vallverdú et al.,   Chapter 5, pages 116-151, IGI Publications, {36 Pages} DOI: 10.4018/978-1-4666-7278-9 .
• Pulkit Verma, Mayank Gupta, Tuhin Bhattacharya, Pradip K. Das, Improving Services using Mobile Agents-based IoT in a Smart City , 2014 International Conference on Contemporary Computing and Informatics (IC3I) , November 27-29, 2014, Mysore, India. [Typhon]
• Manoj Bode, Shashi S. Jha, Shivashankar B. Nair, A Mobile Agent based Autonomous Partial Green Corridor Discovery and Maintenance for Emergency Services amidst Urban Traffic , In Proceedings of the First International Conference on IoT in Urban Space 2014 (URB-IOT ’14), Rome, Italy. ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), ICST, Brussels, Belgium, Belgium, pp. 13-18, ACM Digital Library, {7 Pages} DOI=10.4108/icst.urb-iot.2014.257297 http://dx.doi.org/10.4108/icst.urb-iot.2014.257297   [Tartarus]
• Tonmoy R. Saha, Shivashankar B. Nair, On Grounding Symbols for Robots , 2014 Fourth International Conference on Emerging Applications of Information Technology (EAIT 2014) , Dec. 19-21, 2014, pp. 249-255, IEEE Xplore Digital Library {7 Pages}
• Shashi Shekhar Jha and Shivashankar Nair,  Orchestrating the Sequential Execution of Tasks by a Heterogeneous set of Asynchronous Mobile Agents , [ 12th German Conference on Multiagent System Technologies , Stuttgart, Germany, 23-25th September 2014], Multiagent System Technologies, Lecture Notes in Computer Science,  Volume 8732, 2014, pp 103-120, Springer, {18 Pages} [Typhon]
• Shashi Shekhar Jha, W. W. Godfrey and Shivashankar B. Nair, Stigmergy based Synchronization of a Sequence of Tasks in a Network of Asynchronous Nodes , Cybernetics & Systems: An International Journal , Vol. 45, Issue 5, pp. 373-406, Taylor & Francis Publn. {34 Pages} [Video] [Typhon]
• W. W. Godfrey, Shashi Shekhar Jha and Shivashankar B. Nair,  On Stigmergically Controlling a Population of Heterogeneous Mobile Agents Using Cloning Resource ,  Transactions on Computational Collective Intelligence XIV , Lecture Notes in Computer Science, 2014, pp. 49-70,  Springer, {22 Pages} [Typhon]
• Girish G N, Shrinivasa Naika C.L., Pradip K. Das, Face Recognition using MB-LBP and PCA: A Comparative Study , International Conference on Computer Communication and Informatics (ICCCI -2014), Jan. 03 – 05, 2014, Coimbatore, India.
• Shashi Shekhar Jha, Kunal Shrivastava and Shivashankar Nair, On Emulating Real-world Distributed Intelligence using Mobile Agent based Localized Idiotypic Networks , The First International Conference on Mining Intelligence and Knowledge Exploration (MIKE 2013), Virudhunagar, India, Springer International Publishing, LNAI, Vol. 8284, pp. 487-498, {11 Pages}. [Typhon]
• Shashi Shekhar Jha, Shrinivasa Naika C.L. and Shivashankar Nair, Driving Robots Using Emotions , Transactions on Computational Science XXI, Special Issue on Innovations in Nature-Inspired Computing and Applications, Springer Lecture Notes in Computer Science Vol. 8160, 2013, pp 64-89, {26 Pages}.
• Kunal Shrivastava, Shashi Shekhar Jha and Shivashankar Nair, Autonomous Mobile Robot Navigation using an Artificial Immune System , Proceedings of Conference on Advances In Robotics (AIR ’13), International Conference of the Robotics Society of India, Published byACM, Article 82, {7 Pages} DOI:10.1145/2506095.2506101. [Typhon]
• Saurabh Prajapati, Shrinivasa Naika C.L., Shashi Shekhar Jha and Shivashankar Nair, On Rendering Emotions on a Robotic Face , Proceedings of Conference on Advances In Robotics (AIR ’13) , International Conference of the Robotics Society of India, Published by ACM, Article 19, {7 Pages} DOI:10.1145/2506095.2506151.
• Kunal Shrivastava, Nishant Singhal, Pradip K. Das, Shivashankar B. Nair, “ A Speech Recognition Client-Server Model for Control of Multiple Robots “, Proceedings of Conference on Advances In Robotics (AIR ’13) , International Conference of the Robotics Society of India, Published by ACM, Article 69, {6 Pages} DOI:10.1145/2506095.2506153.
• W.W. Godfrey, Shashi S. Jha, Shivashankar B. Nair, On A Mobile Agent Framework for an Internet of Things ,  Proceedings of the International Conference on Communication System and Technologies, CSNT 2013 , Gwalior, India, Published in IEEE Xplore,  pp. 345-350, {5 Pages}. DOI: 10.1109/CSNT.2013.79 . [Typhon]
• Shashi Shekhar Jha, Shrinivasa Naika C. L., Vibhor Nikhra and Shivashankar B. Nair, “ On Using Emotions in Decision-Making by Situated Robots “, Proceedings of the 12th International Conference on Hybrid Intelligent Systems (HIS 2012), 4 – 7 December, 2012, Pune, India, pp. 372-377,  IEEE Xplore. {6 Pages} DOI: 10.1109/HIS.2012.6421363
• (BEST PAPER AWARD) : Shrinivasa Naika C.L,  Pradip K Das,  Shashi Shekhar Jha, Shivashankar B. Nair,  Automatic Facial Expression Recognition using Extended AR-LBP , Sixth International Conference on Information Processing (ICIP-2012), August 10th – 12th 2012, Communications in Computer and Information Science, Eds. Venugopal, K. R., Patnaik, L. M., Vol. 292, Part 3, Springer Berlin Heidelberg, pp. 244-252. {9 Pages} DOI: 10.1007/978-3-642-31686-9_29
• Shivashankar B. Nair, Bio-inspired Artificial Intelligence , Proceedings of the 3 rd National Conference on Emerging Trends and Applications in Computer Science, (NCETACS – 2012), 30-31st March, 2012, Shillong, India, IEEE Xplore, DOI: 10.1109/NCETACS.2012.6203282
• Shashi S. Jha, Shivashankar B. Nair, A Logic Programming Interface for Multiple Robots , 3 rd National Conference on Emerging Trends and Applications in Computer Science, (NCETACS – 2012), 30-31st March, 2012, Shillong, India, IEEE Xplore, pp.152-156. {5 Pages} DOI:10.1109/NCETACS.2012.6203316
• Shrinivasa Naika C.L,  Pradip K Das, Shivashankar B Nair, Asymmetric Region Local Binary Pattern  For Person-dependent Facial Expression  Recognition , International Conference on Computing, Communication and Applications, ICCCA-2012, 22 – 24th February, 2012, Dindigul, India pp. 1-5, {5 Pages}. (DOI:10.1109/ICCCA.2012.6179199)
• Shivashankar B. Nair, Godfrey, W. Kim, D.H., On Realizing a Multi-Agent Emotion Engine , International Journal of Synthetic Emotions, (IJSE), IGI Global, Vol. No. 2(2), July-December 2011, pp.1-27, {27 Pages}.
• W. Godfrey, Shivashankar B. Nair, A Bio-inspired Technique for Servicing Networked Robots , International Journal of Rapid Manufacturing , Inderscience Enterprises Ltd., Vol. 2, No. 4, 2011, pp. 258-279, {22 Pages} .
• Shrinivasa Naika C.L, Vibhor Nikhra, Shashi Shekhar Jha, Pradip K Das, Shivashankar B Nair, An Intelligent Face Tracking System for Human-Robot Interaction using Camshift Tracking Algorithm , International Journal of Machine Intelligence, ISSN:0975-2927,E-ISSN:0975-9166, Vol.3, Issue 4, pp. 263-267,  Bioinfo Publishers, {5 Pages}.
• Jatin Matani, Shivashankar B. Nair, Typhon – A Mobile Agents Framework for Real world Emulation in Prolog , In C. Sombattheera et al. (Eds.): 5th Multi-disciplinary International Workshop in Artificial Intelligence (MIWAI-2011, Dec. 7-9th), LNAI 7080, pp 261-273, Springer, Heidelberg (ISSN: 1865-0929, ISBN: 978-3-642-22785-1) {13 Pages} DOI: 10.1007/978-3-642-25725-4_23 [Typhon]
• W. Wilfred Godfrey, Shivashankar B. Nair, A Mobile Agent Cloning Controller for Servicing Networked Robots , 2011 International Conference on Future Information Technology, September 16-19th, 2011, IPCSIT vol.13 (2011), pp.91-95, IACSIT Press, Singapore. {5 Pages}.
• S. Borgohain,  Shivashankar B. Nair, Towards a Pictorially Grounded Language for Machine Translation , International Journal of Asian Language Processing, (IJALP), Vol. 20, No. 3, pp. 87-109, {23 Pages}.
• W. Godfrey, Shivashankar B. Nair, A Pheromone based Mobile Agent Migration Strategy for Servicing Networked Robots , Proceedings of the 5th International ICST Conference on Bio-Inspired Models of Network, Information, and Computing Systems, BIONETICS 2010, Bio-Inspired Models of Network, Information, and Computing Systems, Published in the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Volume 87, 2012, pp 533-541, {8 Pages}. DOI: 10.1007/978-3-642-32615-8_52
• W. Godfrey, Shivashankar B. Nair, D.H. Kim, Multi mobile agent multi robot network , Emerging Trends in Robotics and Communication Technologies, 3 to 5 December 2010, Chennai, India.
• Godfrey, W., Shivashankar B. Nair, Mobile Agent Cloning for Servicing Networked Robots , 13th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2010), November 12-15th, Kolkata, India, 2010, published in – Principles and Practice of Multi-Agent Systems, Revised Selected Papers, Lecture Notes in Artificial Intelligence, LNAI, Vol. 7057, Desai, Nirmit; Liu, Alan; Winikoff, Michael (Eds.), Springer-Verlag Berlin Heidelberg, pp.336-339, 2012, {4 Pages}.
• Shivashankar B. Nair, Godfrey, W., Kim, Dong Hwa, Towards a Dynamic Emotional Model , Proceeding of the IEEE International Symposium on Industrial Electronics, 5-8 th July 2009, pp.1932-1936, Seoul,S.Korea, {5 Pages}. (DOI: 10.1109/ISIE.2009.5217935) ( Citations: GoogleScholar 03)
• ( Invited Paper ) Nair, Shivashankar B., Emotional Robot Development: Emotion Agents , Proceedings of the International Conference on Innovation Clusters, 12-13 th May 2009, Daedeok, S. Korea,  pp.478-486, {9 Pages} .
• B. Sukumar, Nair, Shivashankar B., Wireless Sensor Node Localization and Terrain Mapping using Mobile Robots ,  International Conference on Information and Communication Systems, ICICS-2009, Amman, Jordan. 20 th Dec. 2009. (Accepted).
• Shivashankar B. Nair, Towards a Mobile agent based Emotional Being , the 3 rd International Conference for Creation of New Interdisciplinary Paradigms on Human Centred Advanced Intelligent Research and Education, Hu-CARE-2008, Dec. 4-5 th , 2008, Daejeon, S. Korea.
• W. Godfrey, Shivashankar B. Nair, An Immune System based Multi-Robot Mobile Agent Network , Lecture Notes in Computer Science, Springer Berlin / Heidelberg ISSN 0302-9743,Volume 5132/2008, pp. 424-433 (The 7th International Conference on Artificial Immune Systems, 10-13th August 2008, Phuket, Thailand, ICARIS 2008), {10 Pages} (DOI: 10.1007/978-3-540-85072-4_37)  (SCI Conference) (Citations: Scopus 01, GoogleScholar 03) (Also cited in: Review Article: Recent Advances in Artificial Immune Systems: Models and Applications, Dipankar Dasgupta et al, Applied Soft Computing, Volume 11, Issue 2, March 2011, Pages 1574-1587)

2007 and older

• P. Soni, R.K. Rathore, Shivashankar B. Nair, A Framework for the Realization of Networked Robotics and Human Beings , International Symposium on Automation and Robotics in Construction, ISARC 2007, September 19 th -21 st , Kochi, India, pp. 331-338, {8 Pages}.
• Akshat Kumar, Shivashankar B. Nair, An Artificial Immune System based Approach for English Grammar Checking , Lecture Notes in Computer Science, Springer Berlin / Heidelberg ISSN 0302-9743,Volume 4628/2007, pp. 348-357 (The 6th International Conference on Artificial Immune Systems, 26th-29th August, 2007 in Santos/SP, Brazil, Artificial Immune Systems, ICARIS 2007), {10 Pages}. (DOI:10.1007/978-3-540-73922-7_30)  (Citations: GoogleScholar 02)
• R.D. Baruah, Shivashankar B. Nair, EZeeCom: Enabling spontaneous social interactions over mobile ad hoc networks , Proceedings of the ACM sponsored International Wireless Communications and Mobile Computing Conference 2007, IWCMC 2007, August 12-16, 2007, Turtle Bay Resort, Honolulu, Hawaii, ACM Press, ISBN:978-1-59593-695-0,New York, USA, pp.559-564, {7 Pages}. (DOI:10.1145/1280940.1281059 ) (Citations: GoogleScholar 04)
• R.D. Baruah,  Shivashankar B. Nair, RoboSapienNet – Towards Building A Social Network of Human Beings and Robots , Proceedings of the Fourth IEEE International Conference on Wireless and Optical Communications Networks Next Generation Networks July 2, 3 and 4, 2007, Grand Hyatt Singapore, ISBN: 1-4244-1005-3, pp.1-6, {6 Pages}. (DOI:10.1109/WOCN.2007.4284129)
• K V D Pradeep Kumar, M. Saravanan, Shivashankar B. Nair, A Communication Protocol for A mobile Adhoc Network of Robots , Proceedings of the International Conference on Emerging Applications of IT (EAIT 2006), Science City, Kolkata, February 10-11, 2006, Elsevier Press, pp. 223-226, {4 Pages}.
• Chingtham Tejbanta Singh, Shivashankar B. Nair, Modelling a Multi-Agent Mobile Robotic test bed using a Biologically Inspired Artificial Immune System , The Eighth Pacific Rim Workshop on Multi-Agents, PRIMA 2005, 26th-28th September 2005, Kuala Lumpur, Malaysia, Lecture Notes in Computer Science, Springer Berlin/Heidelberg,ISSN 0302-9743 (Print) 1611-3349 (Online), Volume 4078/2009,Multi-Agent Systems for Society (Book), pp.270-283, {14 Pages}.  (DOI: 10.1007/978-3-642-03339-1_22)
• Chingtham Tejbanta Singh, Shivashankar B. Nair An Artificial Immune System for a Multi Agent Robotic System , World Academy of Science, Engineering And Technology, WASET, Volume 6, June 2005, ISSN: 1307-6884, Vol. 6, June 2005, pp. 308-311, {4 Pages}. (Citations: Scopus 04, GoogleScholar-10) (Also Cited in: Artificial Immune Systems and Natural Computing – Applying Complex Adaptive Technologies edited by Hongwei Mo – Chapter V Applications of Artificial Immune Systems in Agents, Idea Group Inc, 2009.)
• N. Toppo  Shivashankar B. Nair, A Framework for Sharing Intelligence Among Mobile Robots on a Network , Session on Network based Intelligent Control, Proceedings of the 2 nd IASTED International Multi-Conference on Automation, Control and Information Technology (ACIT 2005) Novosibirsk, Russia, 20- 24 th June 2005, pp.93-98, {6 Pages}. (Citations: GoogleScholar 01)
• R. P. Badapanda,   Shivashankar B. Nair, Kim Dong Hwa, A Framework for Rapid Deployment of Devices and Robots on a Network , Proceedings of the 1st International Computer Engineering Conference New Technologies for the Information Society ICENCO-2004, (IEEE Egypt Section), Cairo University Cairo, EGYPT, December 27-30, 2004, pp. 625-629, {5 Pages}.( Citations:GoogleScholar 02)
• A.  Sarje,  A. Chawre,  Shivashankar B. Nair, Reinforcement Learning of Player Agents in RoboCup Soccer Simulation , Proceedings of the Fourth International Conference on Hybrid Intelligent Systems, HIS-2004, IEEE-CS Press, December 05-08th, 2004, Kitakyushu, Japan. (DOI:10.1109/ICHIS.2004.81) (Citations: GoogleScholar 08)

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