FingerViP: 指先視覚知覚による実世界器用操作の学習
FingerViP: Learning Real-World Dexterous Manipulation with Fingertip Visual Perception
指先に小型カメラを搭載したモジュールを多指ハンドの各指に取り付け、第三視点カメラと多視点指先視覚を組み合わせた拡散ベースの全身ビジュモータポリシーを提案し、実世界の複雑な操作タスクを実証した。
詳しい要約
1. どんなもの？
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: Zhen Zhang, Weinan Wang, Hejia Sun, Qingpeng Ding, Xiangyu Chu, Guoxin Fang, K. W. Samuel Au
分類: cs.RO
原文アブストラクト
The current practice of dexterous manipulation generally relies on a single wrist-mounted view, which is often occluded and limits performance on tasks requiring multi-view perception. In this work, we present FingerViP, a learning system that utilizes a visuomotor policy with fingertip visual perception for dexterous manipulation. Specifically, we design a vision-enhanced fingertip module with an embedded miniature camera and install the modules on each finger of a multi-fingered hand. The fingertip cameras substantially improve visual perception by providing comprehensive, multi-view feedback of both the hand and its surrounding environment. Building on the integrated fingertip modules, we develop a diffusion-based whole-body visuomotor policy conditioned on a third-view camera and multi-view fingertip vision, which effectively learns complex manipulation skills directly from human demonstrations. To improve view-proprioception alignment and contact awareness, each fingertip visual feature is augmented with its corresponding camera pose encoding and per-finger joint-current encoding. We validate the effectiveness of the multi-view fingertip vision and demonstrate the robustness and adaptability of FingerViP on various challenging real-world tasks, including pressing buttons inside a confined box, retrieving sticks from an unstable support, retrieving objects behind an occluding curtain, and performing long-horizon cabinet opening and object retrieval, achieving an overall success rate of 80.8%. All hardware designs and code will be fully open-sourced.
