Pleurobot is a salamander-inspired robot that is capable of moving in and transitioning between terrestrial and aquatic environments. Some research projects in our lab have demonstrated the effectiveness of vision-guided or human-controlled locomotion transition strategies. However, the present Pleurobot is unable to use similar strategies robustly, especially in outdoor environments, because of lacking vision systems or robust wireless controllers.

In this project, the student will need to add vision systems (e.g., RGB-D camera) for Pleurobot that can operate in amphibious environments. In addition, a robust radio controller is needed to operate the robot in outdoor environments. Alternatively, the student can choose to implement algorithms for the vision system for recognizing terrain and obstacles in real-time. Both systems need to be integrated into the ROS 2 controller running on the onboard computer. The major challenges include the requirements for waterproofing, the limited space for electronics, and the fusion of multiple sensory systems in an embedded system.

The student is expected to have a solid background in circuit design for embedded systems, firmware programming, and familiarity with ROS 2. The student who is interested in this project could send his/her transcript, CV, and materials that can demonstrate his/her past project experience to qiyuan.fu@epfl.ch.

Introduction

This project has been taken.

The spinal cord in many vertebrates contains a central pattern generator (CPG)[1] that can control the physical body to interact with the environment and produce diverse rhythmic motor patterns, such as walking and swimming. And amphibious robots are good biological counterparts of animals for understanding their locomotion. In this project, we would like to use the Deep reinforcement learning framework BRAX[2] in MuJoCo[3] for exploring and designing CPG controllers for a bio-inspired robot Polymander[4] and then verify the controller in real experiments.

Main Contents (PdM contents in bold)

• Migrate the existing CPG-controller into MuJoCo Jax framework to prepare robot simulation experiments
• Use Deep reinforcement learning to train a CPG network for Polymander to walk [, swim, and transition] in different terrains. If needed, access to EPFL scitas computing cluster would be provided.
• Deploy the network on real robots for validation [and reduce the reality gap].

The student is expected to be rigorous and patient, have good programming skills and prior knowledge in reinforcement learning. Having taken the course CS-433 Computational Motor Control is a plus, but not mandatory. Students interested in this project could send CV, transcripts, and materials that can demonstrate project experience, if possible, to supervisors listed above.

Reference

• Ijspeert, Auke Jan, et al. "From swimming to walking with a salamander robot driven by a spinal cord model." science 315.5817 (2007): 1416-1420.

• Freeman, C. Daniel, et al. "Brax--a differentiable physics engine for large scale rigid body simulation." arXiv preprint arXiv:2106.13281 (2021).
• Todorov, Emanuel, Tom Erez, and Yuval Tassa. "Mujoco: A physics engine for model-based control." 2012 IEEE/RSJ international conference on intelligent robots and systems. IEEE, 2012.
• Gevers, Louis, et al. "Investigating the effect of morphology on the terrestrial gaits of amphibious fish using a reconfigurable robot." Bioinspiration & Biomimetics (2025).

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 models 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.).

3D printing of polymers is today a well-implemented process for many applications, including rapid prototyping. Several tens of thousands of parts are produced every year in the 3D printing workshop at SPOT, EPFL's main student makerspace. Most of these parts are produced by FDM 3D printing, using PETG filament. Every year, around 20 kg of thermoplastic waste is thus generated.

As part of its sustainability approach, SPOT has acquired a recycling line (grinding, drying, extrusion) to recycle this waste and produce new 3D printing filaments in-house. Two previous semester projects have optimized the recycling process. However, the filaments obtained can still present random defects (inclusions, diameter variations) which make them unreliable for mass 3d printing. Thus, a device has been designed and built in a previous semester project in order to detect those defects in the filament.

This project aims to further develop the device in order to improve filament quality control and to facilitate the correction of detected defects.

The various steps in the project involves:

• Adding a spool defect mapping function to the existing device.
• Adding a semi-automatic defect correction function.
• Characterizing the entire process and evaluate the device's efficiency.

The student is expected to be rigorous and patient, and to have good programming and prototyping skills (mechanical design, 3D printing, laser cutting, etc). A previous experience prototyping at SPOT is required.

Mobile robots often communicate over the 2.4 GHz band using standard off-the-shelf technologies as WiFi or Bluetooth, or sometimes custom radio protocols either on the 2.4 GHz or 868 MHz ISM bands, both on the UHF part of the radio spectrum. This project aims at evaluating the possibility of using the 169.4 MHz band (VHF) for controlling robots and obtaining telemetry, as it might give much better results in terms of range and transmission through obstacles or water, even if the available bandwidth is much more restricted.

The project involves:

• Identifying an appropriate RF module/chip
• Creating a printed circuit if necessary
• Using a microcontroller to control the RF module and obtain bidirectional communication
• Experiments for range in open air, through obstacles and underwater

Requirements: experience with digital electronics and basic understanding of radio communications and related concepts (e.g. transmission lines, antennas). Previous experience with radio frequency and/or PCB design is a plus.