基于开放世界接触定位的以手为中心的人到机器人轨迹迁移
Hand-centric Human-to-Robot Trajectory Transfer from Video Demonstrations via Object Category-agnostic Temporal Localization
AI总结：
提出HOWTransfer框架，通过接触定位从人类视频中提取接触感知的机器人轨迹，无需物体特定描述，在多样化操作任务中实现86%的成功率。
摘要：
由于嘈杂的手-物体交互、部分观测下的未知物体以及跨实体差异，从人类视频演示中学习仍然具有挑战性。为了解决这些问题，我们提出了\textit{HOWTransfer}（\emph{H}and-\emph{O}bject \emph{O}pen-\emph{W}orld Transfer），这是一个以手为中心的框架，将人类演示提炼为接触感知、分类学信息丰富且多样化的机器人轨迹。\emph{HOWTransfer}不依赖于物体特定描述、视觉语言查询或显式物体状态跟踪，而是通过推理观测到的手-物体交互线索，恢复时间一致的三维手部运动并定位时间接触区间。然后，利用定位的接触起始点将人类抓取意图重定向到多模态平行颚抓取假设，这些假设沿恢复的手腕轨迹传播以生成机器人可执行的运动。最后，轨迹编辑阶段细化接触对齐，并从单个演示生成多样化的可执行变体。跨多种操作任务的实验表明，\emph{HOWTransfer}能够实现准确的接触定位和高质量的机器人运动重定向，成功率为86%，在盲选偏好研究中优于遥操作轨迹。
英文摘要：
Human videos provide a scalable source of demonstrations for robot imitation learning. However, converting them into executable and semantically aligned robot trajectories requires determining \emph{when} task-relevant interactions occur, \emph{how} demonstrated human grasps should be retargeted across embodiments, and \emph{which} post-contact motions should be reproduced. In this paper, we present \textit{HOCALT} (\emph{H}and--\emph{O}bject \emph{C}ategory--\emph{A}gnostic \emph{L}ocalization and \emph{T}ransfer), a hand-centric retargeting framework that unifies 3D hand motion reconstruction, temporal contact localization, and cross-embodiment trajectory generation. Given synchronized stereo videos, \textit{HOCALT} identifies task-relevant contact intervals by jointly reasoning over reconstructed 3D hand articulation and category-agnostic object motion cues. These intervals serve as temporal anchors for initializing cross-embodiment transfer, where the demonstrated human grasps are retargeted into multi-modal robot grasp hypotheses. Within each interval, these hypotheses are then propagated along the demonstrated hand motion, yielding executable trajectories. Finally, we augment the transferred trajectories to generate diverse variants from a single demonstration. Across various tasks, \emph{HOCALT} outperforms VLM-based temporal localization baselines, achieving higher replay success than existing retargeting approaches.
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。
