Project Database

This project is part of an ongoing collaboration with Prof. Eva Naumann at Duke University (USA) to study and model zebrafish sensorimotor integration using simulation and robots. It follows two collaborative studies that were published in Science robotics, see Artificial embodied circuits uncover neural architectures of vertebrate visuomotor behaviors, Energy efficiency and neural control of continuous versus intermittent swimming in a fishlike robot and FARMS: Framework for Animal and Robot Modeling and Simulation.

Depending on the interest of the student, there are several new avenues to be explored. These include: (i) improving the neuromechanical simulation and integrating it into a new simulation framework (FARMS, link), (ii) applying deep reinforcement learning for designing biologically constrained sensory-motor circuits, (iii) investigating new visually-guided behaviors, and/or (iv) programming and improving ZBot for neuroscience related studies.

The project will normally take place at EPFL, but could also happen at Duke University if there is interest.

This master thesis will use paleorobotics to test whether extinct armored mammals could physically generate powerful tail-club strikes. The successful candidate will build MuJoCo and SIMM/OpenSim-type models of a segmented, tendon-driven tail, quantify how joint compliance and soft tissues reshape impact forces, and create an energy dissipation map showing how strike energy is transferred, absorbed, or transmitted through the body. Optional work includes designing a biomimetic hexagonal carapace and a tendon-driven robotic tail prototype. This project will take place both at EPFL and University of Zurich (approx. 60%/40%, TBD). Please submit your resume/CV along with a cover letter detailing your relevant experience and why you are excited about this exceptional thesis opportunity to Auke Ijspeert as well as Ardian Jusufi.

This project has been taken for 2026 fall semester

Background

To improve the indoor autonomy of people with mobility impairments, including wheelchair users, everyday furniture can be equipped with mobility and manipulation capabilities. Such robotic furniture could autonomously reorganize room layouts, clear pathways, transport objects, and grasp and deliver everyday items to users.

Coordinating multiple robotic furniture units in confined indoor environments is challenging because their physical shapes and dimensions cannot be neglected. Unlike point robots, furniture units must account for their full geometry when navigating through narrow spaces and interacting with one another.

Current Progress

Our previously developed algorithm, Velocity Potential Field Modulation (VPFM), has demonstrated strong performance in the dense coordination of polytopic robot swarms in 2D environments. Given the current pose and velocity of each robot, together with its target pose, VPFM generates collision-aware motion commands that guide all robots toward their assigned targets while reducing the risk of collisions and deadlocks.

More information is available in our IEEE Robotics and Automation Letters publication and the corresponding open-source repository:

Paper: IEEE RA-L publication

Project Objectives

1. Adapting VPFM to an indoor maze map with static obstacles.
   An ordinary home environment consists of multiple rooms separated by walls and doors, as well as static furniture pieces. To ensure that each mobile robotic furniture unit can arrive at any reachable position in the home environment, combining VPFM with a global path planner can help the swarm of mobile robotic furniture to overcome bottleneck regions and resolve the deadlock.


2. Conduct simulation-based validation.
   Implement and evaluate the proposed methods in simulation using representative dense indoor scenarios. The evaluation should investigate collision avoidance, deadlock resolution, convergence to target poses, computational efficiency, and the benefits of combining a global path planner with the local VPFM coordinator.