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 Machine 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 | |
Miscellaneous
| 763 – Workload Estimation and Action Classification in Basketball Using IMU Sensors |
| Category: | master project (full-time) | |
| Keywords: | Data Processing, Machine learning, Motion Capture, Programming, Python, Sensor Fusion | |
| Type: | 10% theory, 90% software | |
| Responsible: | (MED 0 1016, phone: 32468) | |
| Description: | n modern basketball, accurately monitoring player workload and identifying specific movement patterns are critical for optimizing performance, reducing injury risk, and tailoring individualized training programs. However, many existing workload assessment tools are not fine-tuned to capture the complex and explosive actions typical in basketball. This project aims to develop a sensor-based system that can estimate physical workload and classify basketball-specific movements using only Inertial Measurement Unit (IMU) sensors. Data will be collected from athletes during structured training sessions, with a focus on high-intensity basketball actions such as rebounds, layups, jump shots, sprints, direction changes, and defensive movements. The primary objective is to create algorithms capable of:
Last edited: 29/08/2025 | |
| 762 – Multimodal sensor fusion for enhanced biomechanical profiling in football: integrating imu and video data from vertical jump tests |
| Category: | master project (full-time) | |
| Keywords: | Data Processing, Image Processing, Machine learning, Motion Capture, Programming, Python, Sensor Fusion | |
| Type: | 100% software | |
| Responsible: | (MED 0 1016, phone: 32468) | |
| Description: | Raw video shows the motion. IMUs reveal the accelerations, orientation. Combined, they unlock new biomechanical precision. This project focuses on developing a sensor fusion framework that synchronizes video recordings and inertial measurement unit (IMU) data to compute enhanced biomechanical metrics from jump tests (bilateral and unilateral CMJ, drop jump). The core aim is to overcome the limitations of each modality alone, combining the spatial richness of video with the temporal and acceleration precision of IMUs. You have access to a dataset consist of 25 players collected inside the lab with an infrared motion tracker system. Traditional biomechanical analysis in sport often relies on expensive lab equipment and manual video inspection. Your work could lay the foundation for next-generation performance monitoring systems that are low-cost, field-deployable, and data-rich. Last edited: 29/08/2025 | |
Mobile robotics
| 732 – Body language control interface of a swarm of assistive robotic furniture using machine learning |
| Category: | semester project, master project (full-time) | |
| Keywords: | C++, Kinect, Linux, Machine learning, Python, Robotics, Vision | |
| Type: | 45% theory, 10% hardware, 45% software | |
| Responsible: | (undefined, phone: 37432) | |
| Description: | Furniture is undergoing a significant transformation, evolving from static objects within the indoor environment into active and mobile entities. These enhanced capabilities not only enable novel modes of interaction but also introduce fundamental questions concerning how such systems should communicate with their users. In collaboration with Prof. Emmanuel Senft from the Human-Centered Robotics and AI group at EPFL IDIAP, and building upon recent advances in assistive robotic furniture developed at EPFL BioRob, this project aims to investigate how robotic furniture can communicate with their user by adapting their motions to achieve defined communication goals. This work builds on established systems in which the poses of both mobile furniture and human users are estimated and tracked using Multi-view Kinect RGB-D cameras combined with a learning-based algorithm. Human motions, or sequence of human poses, can be categorized into different meanings based on current studies of human body language, and can further be classified by the provided visual perception system using either geometrical regulations or learning-based motion recognition algorithm (for example, spatial-temporal graph neural network or transformer). Once the user commands are correctly identified, these commands can be sent to the mobile furniture robot using robot operating system (ROS 2) to execute the commands in order to meet the user requirements in the assistive environment. During the execution, a multi-robot coordination algorithm takes the duty of avoiding collision and resolve deadlocks. The project opens multiple avenues for exploration. Potential directions include the development of more robust learning-based human action recognition algorithms, the design of systematic and user-friendly body language communication protocols, enabling the feedback from the user, the extension of the system to multi-user scenarios and accelerating the whole pipeline. Comprehensive real-world experiments will be conducted to evaluate and validate both the functional capabilities and overall performance of the proposed system. Related work: [1] Real-Time Localization for Closed-Loop Control of Assistive Furniture, Published in: IEEE Robotics and Automation Letters ( Volume: 8, Issue: 8, August 2023) https://ieeexplore.ieee.org/document/10155264 [2] Velocity Potential Field Modulation for Dense Coordination of Polytopic Swarms and Its Application to Assistive Robotic Furniture, Published in: IEEE Robotics and Automation Letters ( Volume: 10, Issue: 7, July 2025) https://ieeexplore.ieee.org/document/11027457 Last edited: 24/08/2025 | |
4 projects found.