Humanoid robots are difficult to train directly on physical hardware, where the process is slow, hazardous, and constrained by mechanical wear. To address this, the HERA project group built an integrated pipeline that links reinforcement learning in a high-fidelity digital twin to real-world execution on the PIB humanoid robot. The team trained a set of sequential PPO policies for a cube-stacking task, running thousands of episodes in parallel within NVIDIA Isaac Lab, and bridged the simulation-to-reality gap by calibrating the twin’s physics against measured joint behavior and adding stereo-vision perception for real workspace sensing. The result is a safer, more scalable route to robot learning that transfers manipulation skills reliably from simulation to hardware.
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