AnyBody: 任意キーポイント指示による全身ヒューマノイドの自由制御
AnyBody: Free-Form Whole-Body Humanoid Control from Arbitrary Keypoint Guidance
任意の身体キーポイントのサブセットを指示として全身ヒューマノイドを制御する統一的なコントローラを提案し、大規模なモーション追跡や下流タスクの学習を実現した。
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著者: Shuning Li, Sikai Li, Jiachen Li, Mingyu Ding
分類: cs.RO, cs.AI
原文アブストラクト
We present AnyBody, a unified whole-body humanoid controller driven by an arbitrary subset of body keypoints chosen at deploy time. Prior physics-based trackers either rely on expensive full-body motion capture and error-prone trajectory retargeting, which bottleneck scalable data collection and policy learning, or decompose upper- and lower-body control into separate hierarchical representations, sacrificing the coordinated whole-body motions that loco-manipulation requires. We close this gap by learning a single latent motion representation that any keypoint subset can address. To achieve this, we first train a privileged teacher tracker on a large unstructured motion corpus and distill it online into a deterministic encoder-decoder student whose latent space is a unit sphere. We then train a transformer keypoint encoder that admits any subset of body keypoints through masked self-attention, aligning it to the privileged latent. Additionally, we treat the frozen decoder as a motor prior and specialize downstream tasks with a lightweight residual corrector in the latent space. We demonstrate the effectiveness of AnyBody by tracking large-scale human motions from arbitrary keypoint subsets, free-form control, flexibly teleoperating, and learning downstream behaviors including locomotion, in-air writing, and obstacle-reach.
