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 Learning 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
Human-exoskeleton dynamics and control
| 735 – Hip exoskeleton to assist walking - multiple projects |
| 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: 21/07/2025 | |
Quadruped robotics
A small excerpt of possible projects is listed here. Highly interested students may also propose projects, or continue an existing topic.
| 747 – Learning frog gaits and their transitions |
| Category: | semester project, master project (full-time) | |
| Keywords: | Artificial muscles, Learning, Locomotion, Python | |
| Type: | 100% software | |
| Responsibles: |
(MED 1 1024, phone: 30563)
(MED 1 1611, phone: 36714) | |
| Description: | During terrestrial locomotion, some frog species display both out-of-phase walking or in-phase hopping limb movements. It has been suggested that changes in these gaits arise to minimize energy consumptions. In this project we will explore this hypothesis by simulating the frog terrestrial locomotion using reinforcement learning. We will use a biomechanical model of the frog adopted with artificial muscles to investigate the optimal gaits for different terrain conditions (low-medium-high ground stiffness). The plantaris longus tendon has been associated with a crucial ability of the frog to store elastic energy during frog jumping. We will test this hypothesis in simulation. The goals can be divided in these subgoals (in order of priority/time): 1. Compute the inertial properties of the frog and URDF file creation 2. Train a neural network controller using reinforcement learning and design of the cost function 3. Testing the ability of the model to walk and hop in simplified scenarios Last edited: 12/11/2024 (revalidated 16/09/2025) | |
Mobile robotics
| 754 – Vision-language model-based mobile robotic manipulation |
| Category: | semester project, master project (full-time), internship | |
| Keywords: | Control, Experiments, Learning, Python, Robotics, Vision | |
| Type: | 30% theory, 10% hardware, 60% software | |
| Responsible: | (MED11626, phone: 41783141830) | |
| Description: | INTRODUCTION Recent vision-language-action models (VLAs) build upon pre-trained vision-language models and leverage diverse robot datasets to demonstrate strong task execution, language-following ability, and semantic generalisation. Despite these successes, VLAs struggle with novel robot setups and require fine-tuning to achieve good performance; however, the most effective way to fine-tune them is unclear, given the numerous 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 equipped with RGBD cameras which is used for data collection, 3) deploy a pre-trained VLA model locally for robot manipulation by using reinforcemnet and imittaion learning, with a focus of household environment, and 4) platform testing, validation and delivery. Excellent programming skill (Python) is a plus. Importance: We have well-documented tutorials of how to use robots, teleoperation for data collection, how to use the HPC cluster, and a complete pipeline to train robot policy. 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 recognised 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 5090 and 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] LeRobot: Making AI for Robotics more accessible with end-to-end learning, https://github.com/huggingface/lerobot [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 [4] Lee BK, Hachiuma R, Ro YM, Wang YC, Wu YH. Unified Reinforcement and Imitation Learning for Vision-Language Models. arXiv preprint arXiv:2510.19307. 2025 Oct 22. 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: 10/11/2025 | |
3 projects found.