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 Computational Neuroscience 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
Computational Neuroscience
| 755 – High-performance encoder-decoder design for computational neural signal processing |
| Category: | semester project, master project (full-time), internship | |
| Keywords: | Computational Neuroscience, Data Processing, Linux, Programming, Python | |
| Type: | 20% theory, 5% hardware, 75% software | |
| Responsible: | (MED11626, phone: 41783141830) | |
| Description: | Background Brain-computer interfaces (BCIs) using signals acquired with intracortical implants have enabled successful high-dimensional robotic device control, making it possible to complete daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems greatly limits their use beyond a few clinical cases. A non-invasive counterpart that requires less intervention and can provide high-quality control would profoundly improve the integration of BCIS into multiple settings, representing a nascent research field known as brain robotics. However, this is challenging due to the inherent complexity of neural signals and difficulties in online neural decoding with efficient algorithms. Moreover, brain signals created by an external stimulus (e.g., vision) are most widely used in BCI-based applications; however, they are impractical and infeasible in dynamic yet constrained environments. A question arises here: "How to circumvent constraints associated with stimulus-based signals? Is it feasible to apply non-invasive BCIS to read brain signals, and how to do so?". To take a step further, I wonder if it would be possible to accurately decode complete, semantic-based command phrases in real time and further achieve seamless and natural brain-robot systems for control and interaction? The project is for a team of 1-2 Master's students, and breakdown tasks will be assigned to each student later according to their skill set. What needs to be implemented and delivered at the end of the project? 1) A method package of brain signal (MEG and EEG) pre-processing and feature formulation 2) An algorithm package of an encoder and a decoder of neural signals. 3) A model of training brain signals with spatial and temporal features. Importance: We have well-documented tutorials on how to use the brain signal dataset, how to use the HPC cluster to train the encoder and decoder, and a complete pipeline to decode EEG-image pairs and MEG-Audio pairs. Please come to office MED01612 Last edited: 11/12/2025 | |
Quadruped robotics
A small excerpt of possible projects is listed here. Highly interested students may also propose projects, or continue an existing topic.
| 769 – Learning Morphology-Specific Emergence of Gaits |
| Category: | master project (full-time) | |
| Keywords: | Biomimicry, Computational Neuroscience, Learning, Python, Simulator | |
| Type: | 20% theory, 80% software | |
| Responsible: | (MED 1 1226, phone: 32658) | |
| Description: | Why do horses and and camels both walk at slow speeds and gallop at fast speeds, but at intermediate speeds horses prefer to trot while camels pace? While gait transitions have been well studied for a given morphology, these models rarely explain when and why animals prefer different or gaits despite being quite similar, or the same gaits despite having very different morphologies. This project tackles this question through the lens of reinforcement learning (RL), with a focus on the role of entrainment between an internal oscillator model and the mechanical dynamics, i.e the morphology. You will explore both top-down and bottom-up coupling mechanisms, unconventional reward functions such as viability measures, and benchmark these approaches across different morphological parameters (e.g length-to-height and width-to-height ratios, mass). Stretch goals can include evaluating the role of active exploration in a hierarchical RL setup, exploring sprawling or bipedal morphologies, changing morphology during learning (e.g. growth), or you may propose something in discussion with the supervisors. NOTE: this is a collaboration project, to be conducted at Cornell University, USA. | |
2 projects found.