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, (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: 05/11/2024 | |
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 | |
| 743 – Quadruped Robot Projects (Several) |
| Category: | semester project, master project (full-time) | |
| Keywords: | Agility, Artificial muscles, Bio-inspiration, C++, Computer Science, Control, Experiments, Learning, Locomotion, Machine learning, Muscle modeling, Online Optimization, Optimization, Programming, Python, Quadruped Locomotion, Robotics, Simulator, Vision | |
| Type: | 10% theory, 20% hardware, 70% software | |
| Responsible: | (MED 1 1024, phone: 37506) | |
| Description: | There are several quadruped robot projects available related to locomotion, jumping, and human-robot interaction, with methodologies including deep reinforcement learning, imitation learning, optimal control, and computer vision. Students who already have experience with deep learning, C++, vision, and who have worked with hardware are especially encouraged to apply. Please send Guillaume your CV, transcript, and explain your motivation on what kind of topics you would be interested in working on (more details = better!). Last edited: 18/07/2024 | |
| 652 – Integrating Learning-Based Control with MPC and CPGs |
| Category: | semester project, master project (full-time), internship | |
| Keywords: | Bio-inspiration, Control, Learning, Locomotion, Optimization, Robotics | |
| Type: | 40% theory, 60% software | |
| Responsible: | (MED 1 1024, phone: 37506) | |
| Description: | Recent years have shown impressive locomotion control of dynamic systems through a variety of methods, for example with optimal control (MPC), machine learning (deep reinforcement learning), and bio-inspired approaches (CPGs). Given a system for which two or more of these methods exist: how should we choose which to use at run time? Should this depend on environmental factors, i.e. the expected value of a given state? Can this help with explainability of what exactly our deep reinforcement learning policy has learned? In this project, the student will use machine learning to answer these questions, as well as integrate CPGs and MPC into the deep reinforcement learning framework. The methods will be validated on systems including quadrupeds and model cars first in simulation, with the goal of transferring the method to hardware. To apply, please email Guillaume with your motivation, CV, and briefly describe your relevant experience (i.e. with machine learning, software engineering, etc.). Last edited: 09/01/2024 (revalidated 18/07/2024) | |
Mobile robotics
| 651 – Autonomous Drifting on Scaled Vehicle Hardware |
| Category: | semester project, master project (full-time), internship | |
| Keywords: | C++, Control, Electronics, Embedded Systems, Experiments, Learning, Optimization | |
| Type: | 10% theory, 60% hardware, 30% software | |
| Responsible: | (MED 1 1024, phone: 37506) | |
| Description: | Controlling vehicles at their limits of handling has significant implications from both safety and autonomous racing perspectives. For example, in icy conditions, skidding may occur unintentionally, making it desirable to safely control the vehicle back to its nominal working conditions. From a racing perspective, drivers of rally cars drift around turns while maintaining high speeds on loose gravel or dirt tracks. In this project, the student will compare several approaches for high speed, dynamic vehicle maneuvers, including NMPC with a standard dynamic bicycle model, NMPC with a dynamic bicycle model + GP residuals, NMPC with learned dynamics (i.e. a NN), and lastly a pure model-free reinforcement learning approach. All approaches will be tested in both simulation as well as on a scaled vehicle hardware platform. To apply, please email Guillaume with your motivation, CV, and briefly describe your relevant experience (i.e. with machine learning, software engineering, etc.). Last edited: 09/01/2024 (revalidated 18/07/2024) | |
5 projects found.