HARE Lab, UC Santa Cruz — September 2024 – June 2025
As an undergraduate researcher at the HARE Lab, I contributed to the development of simulation infrastructure for reinforcement learning-based quadruped locomotion on the Unitree B1 robot. The work focused on building out the Isaac Sim/Lab environment pipeline — designing terrain conditions, configuring simulation tooling, and setting up experiment scaffolding to support future RL policy training.
While I graduated before the lab reached full policy training, I was able to design and implement a variety of terrain environments in Isaac Sim 4.5.0, including the rough terrain shown below.
Terrain conditions were designed to challenge locomotion policies across a range of difficulty levels. Diverse terrain variations included rough uneven ground, slopes, steps, and mixed surfaces — all parameterized to enable curriculum learning during RL training.