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This project has been taken

Machine learning has shown great potential for enabling robots to acquire robust and adaptive locomotion skills. For hyper-redundant robots, such as salamander-inspired robots, this remains challenging because of the high-dimensional body dynamics, the diversity of possible behaviors, and the need to integrate multimodal sensory feedback from both terrestrial and aquatic environments.

This project is an extension of a previous project, in which we will keep exploring learning-based methods for improving salamander robot locomotion in complex amphibious environments. Possible directions include sensor fusion, maneuvering, transition, sim-real transfer, etc. A key goal will be to evaluate whether biological/physical priors can improve learning efficiency, robustness, and smooth transitions between behaviors.

The project is suitable for a highly self-motivated student with complete project experience in machine learning. Familiarity with MuJoCo MJX or other physics simulators is expected. Experience with CPGs, signal processing, reinforcement learning, or sensor-based control will be helpful.

Students who are interested in this project shall send the following materials to the assistant: (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.).

This project has been taken.

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 assistant: (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.).