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.

In nature, animals have many compliant structures that benefit their locomotion. For example, compliant foot/leg structures help adapt to uneven terrain or negotiate obstacles, flexible tails allow efficient undulatory swimming, and muscle-tendon structures help absorb shock and reduce energy loss. Similar compliant structures may benefit salamander-inspired robots as well.

In this study, the student will try simulating compliant structures (the feet of the robot) in Mujoco and optimizing the design. To bridge the sim-to-real gap, the student will first work with other lab members to perform experiments to measure the mechanical properties of a few simple compliant structures. Then, the student needs to simulate these experiments using the flexcomp plugin of Mujoco or theoretical solid mechanics models, and tune the simulation models to match the dynamical response in simulation with the experiments. Afterward, the student needs to optimize the design parameters of the compliant structures in simulation to improve the locomotion performance of the robot while maintaining a small sim-to-real gap. Finally, prototypes of the optimal design will be tested on the physical robot to verify the results.

The student is thus required to be familiar with Python programming, physics engines (preferably Mujoco), and optimization/learning algorithms. The student should also have basic mechanical design abilities to design mechanical structures and perform experiments. Students who have taken the Computational Motor Control course or have experience with data-driven design and solid mechanics would also be preferred.

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

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. To apply, e-mail Steve Heim stating why you are interested in this project (brief, 1-2 sentences each), and attach your CV and transcript.

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.