Project Database

Amphibious robots must move across environments where fluid forces, body contact, and solid structures interact in complex and often unpredictable ways. These interactions are difficult to model accurately, making adaptive and sample-efficient control especially important.

In this project, we will explore how online optimization can improve CPG-based amphibious locomotion controllers. Using onboard sensors such as contact, flow, and proprioceptive sensing, the robot will tune its controller to achieve more agile, efficient, and robust multimodal locomotion. Depending on the project scope, the work may focus on sample-efficient optimization in simulation and simulation-to-robot transfer.

This project is suitable for students who have experience in optimization and MuJoCo simulations. Experience in CPG networks, system identification, signal processing, Arduino/Raspberry Pi programming, and ROS 2 can be very helpful.

Students who are 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 their skills and project experience (such as videos, slides, Git repositories, etc.).

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.).