aiqbench analysis
This research introduces FAME, a reinforcement learning framework that enables full-scale humanoid robots to maintain balance while manipulating objects under unpredictable external forces. By jointly encoding upper-body joint configurations and estimated interaction forces without requiring direct wrist sensors, the method significantly expands the feasible manipulation envelope for bimanual tasks. The approach demonstrates robust performance in both simulation and on physical Unitree H1-2 hardware, addressing a critical stability challenge in advanced robotics.
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.
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 this https URL.
Robotics (cs.RO)
arXiv:2603.08961 [cs.RO]
(or arXiv:2603.08961v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.08961 Focus to learn more arXiv-issued DOI via DataCite
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