FARM: フレーム加速拡張と残差Mixture-of-Expertsによる物理ベース高ダイナミックヒューマノイド制御
FARM: Frame-Accelerated Augmentation and Residual Mixture-of-Experts for Physics-Based High-Dynamic Humanoid Control
フレーム間隔を広げる拡張と残差MoEを組み合わせ、日常的な低ダイナミック動作の精度を保ちつつ爆発的な高ダイナミック動作の追従精度を大幅に改善する物理ベースヒューマノイド制御フレームワークを提案し、専用ベンチマークHDHMも構築した。
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著者: Tan Jing, Shiting Chen, Yangfan Li, Weisheng Xu, Renjing Xu
分類: cs.RO
原文アブストラクト
Unified physics-based humanoid controllers are pivotal for robotics and character animation, yet models that excel on gentle, everyday motions still stumble on explosive actions, hampering real-world deployment. We bridge this gap with FARM (Frame-Accelerated Augmentation and Residual Mixture-of-Experts), an end-to-end framework composed of frame-accelerated augmentation, a robust base controller, and a residual mixture-of-experts (MoE). Frame-accelerated augmentation exposes the model to high-velocity pose changes by widening inter-frame gaps. The base controller reliably tracks everyday low-dynamic motions, while the residual MoE adaptively allocates additional network capacity to handle challenging high-dynamic actions, significantly enhancing tracking accuracy. In the absence of a public benchmark, we curate the High-Dynamic Humanoid Motion (HDHM) dataset, comprising 3593 physically plausible clips. On HDHM, FARM reduces the tracking failure rate by 42.8\% and lowers global mean per-joint position error by 14.6\% relative to the baseline, while preserving near-perfect accuracy on low-dynamic motions. These results establish FARM as a new baseline for high-dynamic humanoid control and introduce the first open benchmark dedicated to this challenge. The code and dataset will be released at https://github.com/Colin-Jing/FARM.
