Join us!

I am regularly seeking motivated MS, PhDs, and Postdoc researchers, as well as visiting researchers!
If you are interested in my research and interested in joining the group, you can send me an email following the instructions below. As the volume of emails can be sometimes daunting, they will help me respond to you in a timely manner.

What I am looking for!?

My research group is small, so I can properly supervise and mentor my team, shape collaborations, boost each person's research visibility, and make sure everyone achieves their full potential. From the prospective candidates, I mainly expect motivation to do research, curiosity to ask right (and wrong) questions, and enthusiasm to work toward solutions. My research spans several disciplines and directions, so I am not looking for any specific background. I will adapt the research project to fit your interests and strengths.

What students should look for?

As a suggestion, MS and Ph.D. applicants should first make sure that the lab works on a topic they are interested in. Research requires enthusiasm for the topic! You should talk to your potential supervisor and ask about the lab structure/culture. How many people are in the lab? Do they stay in the lab? Do students work with senior students (common in big labs), with postdocs, or directly with professors? Is he/she open to your ideas and to help you with them? What have previous lab members done after finishing?

A good idea is to talk to current and former students who may give way important information.

Feel free to contact all my previous/current students (Check-out my CV in the About-Me to find them).

Finding a supervisor is an important decision. Pick wisely. :)

🤖📢 Open Positions - 2025 - Apply Until March 15, 2025
1 Fully funded PhD Studentship in the School of Computer Science - UoN
See more details about the application (link)

For other positions with other PIs, please check the CHART Group homepage - Link

If you are interested in applying for a PhD position with us, please send an email with the subject "[Prospective PhD Candidate - UoN - Topic]" to my email (luis.figueredo AT nottingham.ac.uk).

Please select one of the topics below, and/or feel free to suggest a new one.

To Apply: Interested candidates are invited to submit their application, including a (1) CV with contact information for 2 references, (2) academic transcripts, (3) a cover letter (Max.: 1500 Chars) outlining their research interests and relevant experience, and (4) a research proposal/idea (Max.: 1500 Chars) including the problem you would like to address, the current state-of-the-art and which methods you think would be applicable.

DEADLINE : March 15 (15.03.2025) .

Potential Topics (Feel free to suggest new ones)

Applications Form

This exciting project focuses on developing intelligent robotic systems capable of performing complex, forceful tasks with bimanual coordination. Applications include household tasks and advanced manufacturing — For instance: Opening child-safe medicine containers, cutting, drilling, and manipulating complex objects while using the other hand for stability and adaptability.

Key Highlights:
(Exploring a few of those directions according to your preference — Not all!)

Mastering bimanual Manip.: Explore sequential planning, tool usage, and motion-task strategies;

Robust Execution: Ensure stability and safety during real-time operations;

Whole-body and Beyond-Body: Explore body-contacts and environment points and fixtures to stabilize objects;

Real-Time Motion: Implement real-time controllers and planners under constraints;

Fundamentals of Whole-Body Learning: Improve task and embodiment representation/understanding;

Learning by Demo/Imitation Learning: Learning from demonstrations at the task or motion level.

You must Learn: Bimanual kinematics, geometric methods, geometric and force constraint definition, basic control and motion planning.

Qualifications:

BSc/MSc in robotics, AI, or related fields | Strong programming skills (C++, Python, ML frameworks)

Teamwork and communication abilities | Strong passion for research, and curious personality

Desired: Experience in robotic manipulation, motion planning, and control, simulation environments, and/or use of real-robots.

For more information, check the following research papers:

Switching strategy for flexible task execution using the cooperative dual task-space framework (ICRA, 13)

Manipulation planning under changing external forces (Autonomous Robots, 2020)

Predictive Multi-Agent based Planning and Landing Controller for Reactive Dual-Arm Manipulation (Transaction on Robotics, 2024)
This project aims to rethink and redefine how robots (and us) interact with and manipulate objects in contact-rich scenarios. This is an ambitious problem

involving geometric/Riemannian perspective linear algebra and the SOTA of ML for identification and classification. Applications are literally everywhere in

the real-world, exploring the physical dynamics of contact. For instance, simple activities such as handling multiple door handles and tilt-and-turn windows

demand exploring the system's different articulations with limited force and sequential connection of constraints (one can only open the window once it tilts

enough). Similarly, assembly and disassembly tasks in industry often require tactile exploration followed by sequential manipulation.

Key Highlights:
(Exploring a few of those directions according to your preference — Not all!)

Task Redefinition: Develop/improve frameworks for contact-rich manipulation using geometric methods and Riemannian
manifolds and more intelligent feature space connecting forces and geometric constraints;

Geometric-Oriented Data-Analysis: for different tasks and task classification and identification with set-based constraints;

Learning in Virtual Environment: Exploring VLMs for cues and physics engines for contacts;

Learning in the Physical Domain: Enable task learning under set-based constraints and failure handling;

Safety certified manipulation: in terms of constraints satisfaction (set-based methods) and torque-based methods;

Task Exploration: Under hard safety-constraints, enlarge the soft constraints for improving the task - Touch and find;

What you gotta learn?!: Physical kinematics and dynamics, geometric methods, Riemannian manifolds, geometric and force constraint definition, data-analysis, and machine learning.

Qualifications:

BSc/MSc in robotics, AI, CS, Eng, or related fields | Strong programming skills (e.g., C++, Python, ML, Matlab)

Teamwork and communication abilities | Strong passion for research, and curious personality

Desired: Experience in robotic manipulation, motion planning, and control, simulation environments, and/or usage of real-robots.

For more information, check the following research papers:

CITR: A Coordinate-Invariant Task Representation for Robotic Manipulation (ICRA, 2024)

Integrated Bi-Manual Motion Generation and Control shaped for Probabilistic Movement Primitives (Humanoids, 20222, best paper finalist)

Manipulation planning under changing external forces (Autonomous Robots, 2020)

Shared Autonomy Control for Slosh-Free Teleoperation (IROS, 2023)
Error: empty slot
The research will focus on developing innovative solutions to address the open problem of teaching robots through human demonstrations and multimodalities.

Most approaches to designing plans for complex tasks, such as preprogramming by experienced roboticists, are time-consuming and limiting, especially when considering factors like safety integration, personalization, environmental changes, and task transfer between robots. To overcome these challenges, this research aims to explore novel solutions that enable robots to learn efficiently from human demonstrations, particularly through different user modalities such as natural language processing grounded into the robot's inherent geometric and force constraints. The selected Ph.D. candidate will investigate how different modalities of interaction impact teaching and learning by demonstrating going beyond simple kinesthetic teaching.

Research: will include studying multimodal integration, such as natural language processing combined with visual information (learning from watching) grounded to human-to-robot manipulation transfer. The goal is to develop a framework that requires minimal time from demonstration to deployment on the robot, and minimal cognitive load and expertise from the human teacher. The research will also involve user studies to assess the acceptability and personalization of different modalities, and demonstration methods such as shared-autonomy, teleoperation.

Qualifications:

Master's degree in robotics, artificial intelligence, mechanical engineering, computer science, or a related field.

Excellent programming skills (e.g., C++, Python, Matlab) and experience with robotic simulation environments.

Strong background, expertise or high interest in machine learning tools.

Good communication skills and ability to work collaboratively in a research team.

Desired Experience: Background in robotic manipulation, motion planning, and control, and/or usage of real-robots.

For more information, check the following research papers

Integrated Bi-Manual Motion Generation and Control shaped for Probabilistic Movement Primitives (Humanoids, 20222, best paper award finalist)

Latte: Language trajectory transformer (ICRA, 2023)

Coordinate invariant user-guided constrained path planning with reactive rapidly expanding plane-oriented escaping trees (ICRA, 2022)
The research will focus on developing methods that enable robots to predict and adapt to human kinematic and biomechanical responses during collaborative manipulation, enhancing the efficiency and comfort of human-robot collaboration.

When humans and robots collaborate in manipulating objects, the robot must consider the kinematic and biomechanical responses of the human to optimize its actions. This project aims to develop a method that predicts both the kinematic and biomechanical response of humans during forceful human-robot collaboration (fHRC). These predictions will then be used to plan robot grasps and configurations that minimize the biomechanical load on the human, specifically by reducing predicted muscular effort and enhancing ergonomics.

Research: will include studying the fundamentals of biomechanics for robotics, focusing on human-arm manipulation capabilities and constraints. This includes studying kinematics, dynamics, and existing biomechanics models. The candidate will use this knowledge to define manipulation regions based on different tasks and design controllers and planners for exploring these regions, particularly in the context of sequential tasks.

Qualifications:

Master's degree in robotics, artificial intelligence, computer science, biomechanics or a related field.

Excellent programming skills (e.g., C++, Python, Matlab) and experience with robotic simulation environments.

Good communication skills and ability to work collaboratively in a research team.

Desired Experience: Background in robotic manipulation, motion planning, and control, biomechanics and/or usage of real-robots.

For more information, check the following research papers

Planning to Minimize the Human Muscular Effort during Forceful Human-Robot Collaboration (Transaction on Human-Robot Interaction, 2021)

Human Comfortability: Integrating Ergonomics and Muscular-Informed Metrics for Manipulability Analysis During Human-Robot Collaboration (RAL 2021)

Manipulation planning under changing external forces (Autonomous Robots, 2020)

MS Students @ Nottingham

For other positions with other PIs, please check the CHART Group homepage (link)

If you are interested in applying for an MS position with me for this summer (being from UoN), please send an email with the subject "[Prospective MS Candidate - UoN - Topic]" to my email (luis.figueredo AT nottingham.ac.uk)..

Please find information about some open topics at this link.

Yet, feel free to explore my research and suggest a new one.

To Apply: Interested candidates are invited to submit their application, including a (1) CV and (2) academic transcript.

Please contact me as soon as possible as there are only 2 positions available.

MS Projects @ UoN

Prospective Postdoc Researchers

If you are interested in joining the group as a postdoc, please email me (figueredo AT ieee.org) with the subject "[Prospective Postdoc]". At this moment, I do not have any funded position, but I would be happy to work with you in obtaining external funding for your postdoctoral research.

For PhD Applicants

If you are interested in applying for a PhD position with us, please send an email with the subject "[Prospective PhD Candidate]" to my email (figueredo AT ieee.org). Please include a copy of your CV and some additional information about you and your experience with, e.g., robotics, control, ML, coding (c++, python, matlab), papers etc. No specific experience is required, but knowing your background helps me find a fit for you. Finally, I would also appreciate it if you could briefly mention which research topic in the group interested you and why.

* Candidates who are applying for external fellowships are also very welcome. If you already have an external fellowship, it should be even easier for you to find a spot (in any lab) since you are self-funded.

Master Students

I am regularly looking for motivated MS students who love research and/or robotics. Most of the MS students that I have mentored finished their thesis with an IROS/ICRA/CDC/ACC publication. If you are interested, I will also guide your MS thesis with a publication in mind. However, the research/thesis work should last at least 6 months.
If you are interested, please email me (figueredo AT ieee.org) with the subject "[Prospective MS Student]".

Luis Figueredo