DexH2R：面向人机交接中动态灵巧抓取的基准
DexH2R: A Benchmark for Dynamic Dexterous Grasping in Human-to-Robot Handover
AI总结：
本文提出DexH2R真实世界人机交接数据集及DynamicGrasp解决方案，通过遥操作采集自然灵巧运动数据，并系统评估自回归与扩散策略方法，为动态灵巧抓取研究提供基准。
摘要：
人类与灵巧机器人手之间的交接是人机协作中一项基础但具有挑战性的任务。它需要应对动态环境和各种各样的物体，并要求鲁棒且自适应的抓取策略。然而，由于缺乏高质量、真实世界的人机交接数据集，开发有效的动态灵巧抓取方法的进展受到限制。现有数据集主要关注抓取静态物体或依赖合成的交接运动，这些运动与真实世界的机器人运动模式存在显著差异，从而在适用性上造成了巨大差距。在本文中，我们介绍了DexH2R，一个基于灵巧机器人手构建的、用于人机交接的综合性真实世界数据集。我们的数据集捕获了多样化的交互物体、动态运动模式、丰富的视觉传感器数据以及详细的标注。此外，为了确保自然且类人的灵巧运动，我们利用遥操作进行数据采集，使机器人的运动能够与人类行为和习惯保持一致，这对于智能人形机器人而言是一个关键特性。进一步地，我们提出了一种有效的解决方案DynamicGrasp用于人机交接，并评估了各种最先进的方法，包括自回归模型和扩散策略方法，提供了全面的比较与分析。我们相信，我们的基准将通过提供高质量数据集、有效解决方案和全面的评估指标，推动人机交接研究的进展。
英文摘要：
Handover between a human and a dexterous robotic hand is a fundamental yet challenging task in human-robot collaboration. It requires handling dynamic environments and a wide variety of objects and demands robust and adaptive grasping strategies. However, progress in developing effective dynamic dexterous grasping methods is limited by the absence of high-quality, real-world human-to-robot handover datasets. Existing datasets primarily focus on grasping static objects or rely on synthesized handover motions, which differ significantly from real-world robot motion patterns, creating a substantial gap in applicability. In this paper, we introduce DexH2R, a comprehensive real-world dataset for human-to-robot handovers, built on a dexterous robotic hand. Our dataset captures a diverse range of interactive objects, dynamic motion patterns, rich visual sensor data, and detailed annotations. Additionally, to ensure natural and human-like dexterous motions, we utilize teleoperation for data collection, enabling the robot's movements to align with human behaviors and habits, which is a crucial characteristic for intelligent humanoid robots. Furthermore, we propose an effective solution, DynamicGrasp, for human-to-robot handover and evaluate various state-of-the-art approaches, including auto-regressive models and diffusion policy methods, providing a thorough comparison and analysis. We believe our benchmark will drive advancements in human-to-robot handover research by offering a high-quality dataset, effective solutions, and comprehensive evaluation metrics.
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。
