FAME: Force-Adaptive RL for Expanding the Manipulation Envelope of a Full-Scale Humanoid
FAME is a force-adaptive reinforcement learning framework designed to improve balance in humanoid bimanual manipulation by handling external hand forces. It conditions a standing policy using a latent context vector that encodes upper-body joint configurations and interaction forces. This allows the system to adapt its lower-body control strategy robustly across diverse arm setups, leading to significantly higher standing success rates in both simulation and real-world tests.