Curriculum Reinforcement Learning with AI Agents and Parallelized Simulation
I created a GPU-accelerated simulation and training pipeline for the humanoid robot BRUCE using MuJoCo-MJX, enabling massive parallel environments for curriculum reinforcement learning. Accurate dynamics modeling of complex mechanisms and backlash were crucial for sim-to-real transfer and policy robustness on hardware. AI agents, RAG, and statc schemas were integrated to iteratively construct and refine training curriculum. The policies were deployed onto our custom robot BRUCE, a kid-sized humanoid with 3 modes of parallel actuation.