VibeAct: 振動から行動へ - 接触の多い反応的ロボット器用操作のための手法
VibeAct: Vibration to Actions for Contact-Rich Reactive Robot Dexterity
圧電マイクで取得した実振動信号を、シミュレーションで共有可能な接触・滑り表現に変換し、実世界とシミュレーションのギャップを埋めることで、器用なロボットハンドのポリシー学習を可能にするフレームワークを提案した。
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著者: Yuemin Mao, Uksang Yoo, Jean Oh, Jonathan Francis, Jeffrey Ichnowski
分類: cs.RO
原文アブストラクト
Dexterous manipulation depends on contact events that are fast, local, and often visually occluded. Piezoelectric microphones offer a compact and high-bandwidth way to sense these interactions, but the resulting vibro-acoustic signals are difficult to simulate faithfully enough for end-to-end sim-to-real policy learning on dexterous robot hands. We propose VibeAct, a framework that bridges real vibrotactile sensing and simulation-based reinforcement learning through a shared physical representation of contact and slip. In the real world, we embed piezoelectric microphones into a dexterous robot hand and collect vibro-acoustic data through teleoperation, then replay the recordings in a calibrated digital clone to automatically label per-finger contact and slip. A tactile estimator learns to predict contact and slip from real microphone waveforms, while manipulation policies are trained in simulation on the same representation computed directly from simulated contacts. This decoupling lets policies exploit rapid tactile feedback without simulating raw audio. Across five contact-rich tasks spanning regrasping, in-hand reorientation, and insertion, VibeAct consistently outperforms a proprioception-and-point-cloud baseline in simulation, with the largest gains on tasks requiring sustained reactive control, where the continuous slip-magnitude channel proves the most informative observation. The learned policies transfer to a physical dexterous hand-arm platform, improving success rates on deployed tasks. Project videos and additional details are at https://vibeact.github.io/.
