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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 Experiments 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

759 – Snakes vs 1guilla - a kinematic and energetic comparison
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Category:master project (full-time)
Keywords:Bio-inspiration, Data Evaluation, Data Processing, Experiments
Type:20% theory, 40% hardware, 40% software
Responsible: (MED 0 2326, phone: 38499)
Description:

Snakes use undulatory swimming to move efficiently through water, and different species show distinct patterns depending on their morphology and environment. With datasets available from live swimming tests and access to an undulatory robot, we can combine data analysis and physical experiments to explore their performance systematically and address biological questions.

Project Description:

The goal of this project is to analyse and replicate the diversity of swimming gaits in snakes using kinematic data from different species and a robot to explore insights into energy consumption. We’ll use reduced-order modelling to extract dominant motion patterns and look for structure in the data, for example, clustering gaits by species or lifestyle. Based on these results, we’ll test a few representative cases on a robotic platform to evaluate how well the robot can replicate biological gaits and how performance (e.g. speed, efficiency) varies with different strategies.

Specific Goals:

  • Analyse snake swimming data to extract key gait parameters (e.g. curvature, amplitude, frequency)
  • Perform reduced-order modelling to identify dominant gait modes.
  • Cluster the gaits by species, morphology, or habitat.
  • Select a few representative gaits and implement them on an undulatory robot.
  • Run experiments to evaluate robotic performance across gaits.
  • Compare biological and robotic results to identify general trends.


Last edited: 14/07/2025
756 – Imitating animal behavior on a bio-inspired robot with reinforcement learning
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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)

  • Migrate the existing CPG-controller into MuJoCo Jax framework to prepare robot simulation experiments
  • Use Deep reinforcement learning to train a CPG network for Polymander to walk [, swim, and transition] in different terrains. If needed, access to EPFL scitas computing cluster would be provided.
  • Deploy the network on real robots for validation [and reduce the reality gap].

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

  • Ijspeert, Auke Jan, et al. "From swimming to walking with a salamander robot driven by a spinal cord model." science 315.5817 (2007): 1416-1420.

  • Freeman, C. Daniel, et al. "Brax--a differentiable physics engine for large scale rigid body simulation." arXiv preprint arXiv:2106.13281 (2021).
  • Todorov, Emanuel, Tom Erez, and Yuval Tassa. "Mujoco: A physics engine for model-based control." 2012 IEEE/RSJ international conference on intelligent robots and systems. IEEE, 2012.
  • Gevers, Louis, et al. "Investigating the effect of morphology on the terrestrial gaits of amphibious fish using a reconfigurable robot." Bioinspiration & Biomimetics (2025).


Last edited: 18/06/2025
758 – Optimization of compliant structure designs in a salamander robot using physics simulation
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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

Human-exoskeleton dynamics and control

735 – Hip exoskeleton to assist walking - multiple projects
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Category:semester project, master project (full-time), bachelor semester project, internship
Keywords:Bio-inspiration, C, C++, Communication, Compliance, Control, Data Processing, Dynamics Model, Electronics, Experiments, Inverse Dynamics, Kinematics Model, Learning, Locomotion, Machine learning, Online Optimization, Optimization, Programming, Python, Robotics, Treadmill
Type:30% theory, 35% hardware, 35% software
Responsible: (MED 3 1015, phone: 31153)
Description:Exoskeletons have experienced an unprecedented growth in recent years and hip-targeting active devices have demonstrated their potential in assisting walking activities. Portable exoskeletons are designed to provide assistive torques while taking off the added weight, with the overall goal of increasing the endurance, reducing the energetic expenditure and increase the performance during walking. The design of exoskeletons involves the development of the sensing, the actuation, the control, and the human-robot interface. In our lab, a hip-joint active hip orthosis (“eWalk”) has been prototyped and tested in recent years. Currently, multiple projects are available to address open research questions. Does the exoskeleton reduce the effort while walking? How can we model human-exoskeleton interaction? How can we design effective controls? How can we optimize the interfaces and the control? Which movements can we assist with exoskeletons? To address these challenges, the field necessitates knowledge in biology, mechanics, electronics, physiology, informatics (programming, learning algorithms), and human-robot interaction. If you are interested in collaborating in one of these topics, please send an email to giulia.ramella@epfl.ch with (1) your CV+transcripts, (2) your previous experiences that could be relevant to the project, and (3) what interests you the most about this research topic (to be discussed during the interview).

Last edited: 22/01/2025

Mobile robotics

754 – Vision-language model-based mobile robotic manipulation
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Category:master project (full-time), internship
Keywords:Control, Experiments, Learning, Python, Robotics, Vision
Type:20% theory, 20% hardware, 60% software
Responsible: (MED11626, phone: 41783141830)
Description:INTRODUCTION Recent vision-language-action models (VLAs) build upon pretrained vision-language models and leverage diverse robot datasets to demonstrate strong task execution, language following ability, and semantic generalization. Despite these successes, VLAs struggle with novel robot setups and require fine-tuning to achieve good performance, yet how to most effectively fine-tune them is unclear, given many possible strategies. This project aims to 1) develop a customised mobile robot platform that is composed of a customised and ROS2-based mobile base and robot arms with 6DOF (ViperX 300 S and Widowx 250), and 2) establish a vision system equiped with RGBD cameras which is used for data collection, 3) deploy a pre-trained VLA model locally for robot manipulation with a focus of household environment, and 4) platform testing, validation and delivery. Excellent programming skill (Python) is a plus. For applicants not from EPFL, to obtain the student status at EPFL, the following conditions must be fulfilled (an attestation has to be provided during the online registration): [1] To be registered at a university for the whole duration of the project [2] The project must be required in the academic program and recognized by the home university [3] The duration of the project is a minimum of 2 months and a maximum of 12 months [4] To be accepted by an EPFL professor to do a project under his supervision For an internship, six months at least is suggested. WHAT WE HAVE: [1] Ready-and-easy-to-use robot platforms: including ViperX 300S and WidowX-250, configured with 4 RealSense D405, various grippers, and mobile robot platform [2] Computing resources: TWO desktop PC with NVIDIA GPU 4090 [3] HPC cluster with 1000h/month on NVIDIA A100 and A100fat : can use 1000 hours of A100 and A100 fat NVIDIA GPU every month, supports large-scale training and fine-tuning. Interested students can apply by sending an email to sichao.liu@epfl.ch. Please attach your transcript and past/current experience on the related topics. The position is open until we have final candidates. Otherwise, the position will be closed. Recommend to read: [1] https://www.physicalintelligence.company/blog [2] Kim, Moo Jin, Chelsea Finn, and Percy Liang. "Fine-tuning vision-language-action models: Optimizing speed and success." arXiv preprint arXiv:2502.19645 (2025). [3] https://docs.trossenrobotics.com/aloha_docs/2.0/specifications.html Benchmark: [1] LeRobot: Making AI for Robotics more accessible with end-to-end learning [2] DROID: A Large-Scale In-the-Wild Robot Manipulation Dataset [3] DiT-Block Policy: The Ingredients for Robotic Diffusion Transformers [4] Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Last edited: 30/06/2025

5 projects found.

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