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 Data Processing 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

767 – Data collection pipeline for sensorized amphibious robot experiments
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Category:semester project, master project (full-time)
Keywords:3D, C, C++, Communication, Computer Science, Data Processing, Experiments, Firmware, Image Processing, Motion Capture, Programming, Python, Robotics, Synchronization, Vision
Type:5% theory, 20% hardware, 75% software
Responsible: (MED 1 1626, phone: 38676)
Description:

This project has been taken

In this project, the student will work closely with the other team members to develop data collection pipelines during the experiments of a sensorized amphibious robot and, optionally, use them to collect and analyze experimental data. Specifically, the student needs to:

  • Build a multi-camera system for tracking 3-D kinematics of the robots, ideally in real time. The system is expected to work in both indoor and outdoor experiments. We already have a few working setups, and the student needs to replicate them using new hardware, calibrate the system, and implement real-time tracking nodes in ROS2.
  • Synchronize data collected from multiple resources: cameras, force sensors, motors, etc.
  • Visualize the data collected in Mujoco viewer, RViz, Blender, or other 3D visualizers.
  • Help collect experimental data.
  • (For master project only) Help analyze data or use learning algorithms to find underlying patterns.

The student is expected to be familiar with programming in C/C++ and Python, using ROS2, and robot kinematics. Experience with Docker, Linux kernel, communication protocols, and computer vision algorithms would be a bonus.

The student who is interested in this project shall send the following materials to the assistant: (1) resume, (2) transcript showing relevant courses and grades, and (3) other materials that can demonstrate their skills and project experience (such as videos, slides, code repositories, etc.).



Last edited: 17/01/2026

Computational Neuroscience

755 – High-performance encoder-decoder design for computational neural signal processing
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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

2 projects found.