Robo-GPC: Compliant Robotic Control with Generative Pretrained Controllers

Humanoid robots operating in human environments must interact safely and robustly with both humans and their surroundings. They must remain stable under disturbances, respond compliantly to external forces, and recover naturally from falls. We introduce Robotic Generative Pretrained Controller (Robo-GPC), a discrete generative controller that learns motor skills from large-scale human motion data and adapts to downstream whole-body control tasks through parameter-efficient fine-tuning. Robo-GPC represents motor skills in a compact Finite Scalar Quantization (FSQ) latent space and models their distribution with an autoregressive transformer decoder. By reusing the learned skill distribution during task adaptation, Robo-GPC produces natural behaviors while supporting a wide range of downstream control tasks, providing a general framework for safer humanoid deployment. Deployed on a Unitree G1 humanoid, Robo-GPC enables robust whole-body control while responding naturally to pushes and pulls and recovering from large disturbances and falls. The supplementary videos are present in https://anonymous.4open.science/r/robogpc/index.html.