FAME: Force-Adaptive RL for Expanding the Manipulation Envelope of a Full-Scale Humanoid

arXiv:2603.08961v2 Announce Type: replace Abstract: Maintaining balance under external hand forces is critical for humanoid bimanual manipulation, where interaction forces propagate through the kinematic chain and constrain the feasible manipulation envelope. We propose FAME, a force-adaptive reinforcement learning framework that conditions a standing policy on a learned latent context encoding upper-body joint configuration and bimanual interaction forces jointly, since the base moment a load induces depends on the arm configuration through which it acts. Training applies isotropically sampled 3D forces at each hand under an upper-body pose curriculum, exposing the policy to manipulation-induced perturbations across continuously varying arm configurations. At deployment the interaction force is not measured but reconstructed online from joint torques and states through rigid-body inverse dynamics, requiring no wrist force/torque sensing. We evaluate over 100 upper-body configurations under swept hand forces, scoring each trial by a task-level criterion that requires the robot both to remain upright and to hold its hands near where the task placed them; all such results run with the estimated force in the loop. At a 150,mm tolerance FAME reaches 38.9% task success, against 16.6% for a policy given the same force without encoding, 4.3% for a pose-conditioned curriculum policy, and 24.7% for an adversarially trained locomotion policy, which stays upright but recovers by stepping and so relocates the hands. We further demonstrate transfer to task-generated interaction forces in a MuJoCo kitchen environment, and to asymmetric and bimanual loading on a full-scale Unitree H1-2. Code and videos are available on the https://correlllab.github.io/fame_website.

Source: arXiv cs.RO | 2026-10-02