Project Database
This page contains the database of possible research projects for master and bachelor students in the Biorobotics Laboratory (BioRob). Visiting students are also welcome to join BioRob, but it should be noted that no funding is offered for those projects (see https://biorob.epfl.ch/students/ for instructions). To enroll for a project, please directly contact one of the assistants (directly in his/her office, by phone or by mail). Spontaneous propositions for projects are also welcome, if they are related to the research topics of BioRob, see the BioRob Research pages and the results of previous student projects.
Search filter: only projects matching the keyword Simulator are shown here. Remove filter
Amphibious robotics
Computational Neuroscience
Dynamical systems
Human-exoskeleton dynamics and control
Humanoid robotics
Miscellaneous
Mobile robotics
Modular robotics
Neuro-muscular modelling
Quadruped robotics
Amphibious robotics
756 – Imitating animal behavior on a bio-inspired robot with reinforcement learning |
Category: | semester project, master project (full-time) | |
Keywords: | Experiments, Learning, Programming, Robotics, Simulator | |
Type: | 15% theory, 10% hardware, 75% software | |
Responsibles: |
(MED 1 1611, phone: 36620)
(MED 1 1611, phone: -) (MED 1 1626, phone: 38676) | |
Description: | Introduction This project has been taken. The spinal cord in many vertebrates contains a central pattern generator (CPG)[1] that can control the physical body to interact with the environment and produce diverse rhythmic motor patterns, such as walking and swimming. And amphibious robots are good biological counterparts of animals for understanding their locomotion. In this project, we would like to use the Deep reinforcement learning framework BRAX[2] in MuJoCo[3] for exploring and designing CPG controllers for a bio-inspired robot Polymander[4] and then verify the controller in real experiments. Main Contents (PdM contents in bold)
The student is expected to be rigorous and patient, have good programming skills and prior knowledge in reinforcement learning. Having taken the course CS-433 Computational Motor Control is a plus, but not mandatory. Students interested in this project could send CV, transcripts, and materials that can demonstrate project experience, if possible, to supervisors listed above. Reference
Last edited: 18/06/2025 |
758 – Optimization of compliant structure designs in a salamander robot using physics simulation |
Category: | master project (full-time) | |
Keywords: | Bio-inspiration, Biomimicry, Compliance, Dynamics Model, Experiments, Locomotion, Optimization, Programming, Python, Robotics, Simulator, Soft robotics | |
Type: | 30% theory, 20% hardware, 50% software | |
Responsibles: |
(MED 1 1611, phone: 36620)
(MED 1 1626, phone: 38676) | |
Description: | In nature, animals have many compliant structures that benefit their locomotion. For example, compliant foot/leg structures help adapt to uneven terrain or negotiate obstacles, flexible tails allow efficient undulatory swimming, and muscle-tendon structures help absorb shock and reduce energy loss. Similar compliant structures may benefit salamander-inspired robots as well. In this study, the student will try simulating compliant structures (the feet of the robot) in Mujoco and optimizing the design. To bridge the sim-to-real gap, the student will first work with other lab members to perform experiments to measure the mechanical properties of a few simple compliant structures. Then, the student needs to simulate these experiments using the flexcomp plugin of Mujoco or theoretical solid mechanics models and tune the simulation models to match the dynamical response in simulation with the experiments. Afterward, the student needs to optimize the design parameters of the compliant structures in simulation to improve the locomotion performance of the robot while maintaining a small sim-to-real gap. Finally, prototypes of the optimal design will be tested on the physical robot to verify the results. The student is thus required to be familiar with Python programming, physics engines (preferably Mujoco), and optimization/learning algorithms. The student should also have basic mechanical design abilities to design models and perform experiments. Students who have taken the Computational Motor Control course or have experience with data-driven design and solid mechanics would also be preferred. The student who is interested in this project shall send the following materials to the assistants: (1) resume, (2) transcript showing relevant courses and grades, and (3) other materials that can demonstrate your skills and project experience (such as videos, slides, Git repositories, etc.). Last edited: 17/06/2025 |
Mobile robotics
744 – Multi-robot coordination of assistive furniture swarm in multiple layers using velocity potential field modulation |
Category: | master project (full-time) | |
Keywords: | 3D, Control, Motion Capture, Programming, Python, Robotics, Simulator | |
Type: | 40% theory, 10% hardware, 50% software | |
Responsible: | (undefined, phone: 37432) | |
Description: | We are exploring the concept of a mobile furniture swarm that are intended to assist users with limited mobility in their daily indoor activities. To facilitate multiple uses of limited space, mobile furniture pieces can autonomously rearrange their formation (e.g., setups for meetings, parties, or cleaning). To enhance daily autonomy, assistive furniture can actively move out of the way for a wheelchair user passing by, or follow the user to help carrying objects. Our previous algorithm, Velocity Potential Field Modulation (VPFM), has been proposed to deal with the dense coordination problem of a polytopic swarm in 2D scenarios. For more information, please check out our recent publication in IEEE RA-L: -- Title: Velocity Potential Field Modulation for Dense Coordination of Polytopic Swarms and Its Application to Assistive Robotic Furniture -- Paper: https://ieeexplore.ieee.org/document/11027457 -- Code: https://github.com/Mikasatlx/VPFM-BioRob-EPFL In this master thesis, we will focus on extending VPFM to multiple layers (or height), which can increase the efficiency of using the 3D space. For example, the lower table can move through a higher table, and the seat of a chair can go under a higher table, but the back of a chair can not. The current framework is to couple the coordination behavior over multiple layers, and introduce stochastic component to break the deadlocks/oscillations. We will conduct both simulations and real-world experiments (using VICON motion capture system) to evaluate its effectiveness and real-time performance. For thesis with meaningful results, we will aim for a submission to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026). Last edited: 15/06/2025 |
3 projects found.