Abstract
Humanoid motion trackers reproduce diverse behaviors, but sharing a policy across bodies requires accounting for differences in structure, morphology and dynamics. We present MimicAll, a reinforcement-learning framework for shared whole-body motion tracking across heterogeneous humanoids. A structured embodiment descriptor is encoded into global and joint-aligned conditions, which are combined with observation history and body-matched motion references to predict joint-target offsets. Physics-consistent embodiment randomization, body-calibrated retargeting and heterogeneous simulation support training across 4,096 variants from 16 humanoid families. We introduce a benchmark of 464 bodies across five distribution strata to evaluate motion generalization, within-family embodiment shifts and unseen-family transfer. Controlled ablations show that embodiment information improves motion completion with simple concatenation, while self-attention yields the lowest global position errors. With equal training transitions, shared training achieves 99.4% 30-second success on randomized G1 bodies in 100STYLE, versus 42.9% for a single-body specialist. With Flat-MLP encoding and concatenation, continued training on a motion subset excluding obstacle categories achieves 98.3% and 93.5% 30-second success on 100STYLE for sampled training bodies and out-of-distribution variants within seen families, respectively. On an eight-motion LAFAN1 subset excluding ground and get-up sequences across 464 bodies, mismatched body conditions reduce self-attention's 30-second success from 61.7% to 47.0%, demonstrating a closed-loop benefit from embodiment information.
Abstract (translated)
URL
https://arxiv.org/abs/2610.33508
