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In this project, the student will work closely with the other team members to develop data collection pipelines during the experiments of a sensorized amphibious robot and, optionally, use them to collect and analyze experimental data. Specifically, the student needs to:

• Build a multi-camera system for tracking 3-D kinematics of the robots, ideally in real time. The system is expected to work in both indoor and outdoor experiments. We already have a few working setups, and the student needs to replicate them using new hardware, calibrate the system, and implement real-time tracking nodes in ROS2.
• Synchronize data collected from multiple resources: cameras, force sensors, motors, etc.
• Visualize the data collected in Mujoco viewer, RViz, Blender, or other 3D visualizers.
• Help collect experimental data.
• (For master project only) Help analyze data or use learning algorithms to find underlying patterns.

The student is expected to be familiar with programming in C/C++ and Python, using ROS2, and robot kinematics. Experience with Docker, Linux kernel, communication protocols, and computer vision algorithms would be a bonus.

The student who is 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, code 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 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.).