OmniDP: 在超广角大工作空间中实现人形机器人的全方位3D感知
OmniDP: Beyond-FOV Large-Workspace Humanoid Manipulation with Omnidirectional 3D Perception
AI总结：
OmniDP通过360度全景点云感知和时间感知注意力池化机制，实现人形机器人在大工作空间中的稳健操作，优于传统深度相机方法。
摘要：
人形机器人在无结构环境中进行精细操作的部署仍然具有挑战性，因为感知限制会限制有效的工作空间。在物理限制阻止机器人重新定位的情况下，保持全方位感知比颜色或语义信息更为关键。尽管最近在视觉-运动策略学习方面的进展提高了操作能力，但传统RGB-D解决方案存在狭窄的视野（FOV）和自我遮挡问题，需要频繁的基座移动，这会引入运动不确定性和安全风险。现有的扩展感知方法，包括主动视觉系统和第三视角相机，引入了机械复杂性、校准依赖性和延迟，阻碍了可靠的实时性能。在本工作中，我们提出了OmniDP，一种端到端的激光雷达驱动的3D视觉-运动策略，能够在大工作空间中实现稳健的操作。我们的方法通过时间感知注意力池化机制处理全景点云，高效编码稀疏3D数据的同时捕捉时间依赖性。这种360度的感知使机器人能够在宽广的区域内与物体交互而无需频繁重新定位。为了支持策略学习，我们开发了一个全身遥控系统，以高效地在全身协调上收集数据。在模拟和真实环境中的大量实验表明，OmniDP在大工作空间和杂乱场景中实现了稳健的性能，优于依赖于自体深度相机的基线方法。
英文摘要：
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° 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
