OmniDP: 全方位3D知覚による広視野・大作業空間でのヒューマノイド操作
OmniDP: Beyond-FOV Large-Workspace Humanoid Manipulation with Omnidirectional 3D Perception
RGB-Dカメラの狭い視野や自己遮蔽の問題を解決するため、LiDARによる全方位点群を時間的注意プーリングで処理するエンドツーエンドの3D視覚運動ポリシーを提案し、ロボットの再配置なしで広い作業空間での操作を可能にした。
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著者: Pei Qu, Zheng Li, Yufei Jia, Ziyun Liu, Liang Zhu, Haoang Li, Jinni Zhou, Jun Ma
分類: cs.RO
原文アブストラクト
The deployment of humanoid robots for dexterous manipulation in unstructured environments remains challenging due to perceptual limitations that constrain the effective workspace. In scenarios where physical constraints prevent the robot from repositioning itself, maintaining omnidirectional awareness becomes far more critical than color or semantic information.While recent advances in visuomotor policy learning have improved manipulation capabilities, conventional RGB-D solutions suffer from narrow fields of view (FOV) and self-occlusion, requiring frequent base movements that introduce motion uncertainty and safety risks. Existing approaches to expanding perception, including active vision systems and third-view cameras, introduce mechanical complexity, calibration dependencies, and latency that hinder reliable real-time performance. In this work, We propose OmniDP, an end-to-end LiDAR-driven 3D visuomotor policy that enables robust manipulation in large workspaces. Our method processes panoramic point clouds through a Time-Aware Attention Pooling mechanism, efficiently encoding sparse 3D data while capturing temporal dependencies. This 360{\deg} perception allows the robot to interact with objects across wide areas without frequent repositioning. To support policy learning, we develop a whole-body teleoperation system for efficient data collection on full-body coordination. Extensive experiments in simulation and real-world environments show that OmniDP achieves robust performance in large-workspace and cluttered scenarios, outperforming baselines that rely on egocentric depth cameras.
