VAIC: 視覚誘導によるヒューマノイドの俊敏な物体インタラクション制御 - 分離コマンドを用いて
VAIC: Vision-Guided Humanoid Agile Object Interaction Control via Decoupled Commands
ヒューマノイドロボットが、深度カメラと自己状態のみから、箱運びやスケートボードなどの多様な動的物体操作を単一のポリシーで実行できるようにする、教師-生徒蒸留に基づく制御フレームワークを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Dongting Li, Qianyang Wu, Xingyu Chen, Liang Li, Yuhang Lin, Sikai Wu, Guoyao Zhang, Mingliang Zhou, Diyun Xiang, Qiang Zhang, Renjing Xu, Jianzhu Ma
分類: cs.RO
原文アブストラクト
Humanoid robots hold immense potential for real-world assistance, yet agile interaction with objects in unstructured environments demands tightly coupled whole-body coordination. Despite recent advancements, current controllers face a critical deployment gap. They rely heavily on dense reference trajectories and perfect state observability, which inherently limits physical generalization. We present Vision Guided Agile Interaction Control (VAIC), a unified framework that bridges this gap by operating exclusively on onboard depth, historical proprioception, and a decoupled user command interface. VAIC employs a two-stage distillation paradigm. First, a privileged teacher policy masters diverse interaction skills using precise object kinematics and exact environmental states. Second, a deployable student policy distills these capabilities by replacing full body tracking with velocity targets across multiple axes and an interaction indicator for each frame. The student utilizes a recurrent object adaptation module to implicitly infer unobservable object dynamics from raw depth streams and proprioception. Evaluations and real-world deployments on the humanoid robot demonstrate that a single VAIC policy successfully executes highly diverse dynamic tasks. These tasks include box carrying, cart interaction, and skateboarding, consistently outperforming baselines and advancing autonomous humanoid deployment.
