VR-DAgger: 用于灵巧数据收集和不确定性引导的在线策略校正的沉浸式VR
VR-DAgger: Immersive VR for Dexterous Data Collection and Uncertainty-Guided On-Policy Correction
AI总结：
提出VR-DAgger框架，通过VR应用进行灵巧遥操作和数据收集，利用MC Dropout不确定性评分选择关键失败片段进行在线校正，在灵巧操作任务上相比行为克隆提升高达23个百分点，并减少约40%的样本收集时间。
摘要：
从示范中学习对于机器人操作是有效的，但收集足够的任务特定数据仍然是一个主要瓶颈。在分布偏移下，小误差会累积，性能下降，专家时间往往花费在冗余、低价值的修正上，而不是少数关键失败案例。我们提出了VR-DAgger，一个以沉浸式VR应用为中心的人机协作框架，用于灵巧遥操作、示范收集和选择性策略校正。VR客户端提供直观的手部控制和同步场景可视化，而后台工作站运行仿真和学习，实现无需操作员持续监督的自主部署。我们使用蒙特卡洛（MC）Dropout在Isaac Lab部署扩散策略时对不确定性进行评分，并选择信息量大的失败片段进行校正。这些片段在VR中作为剪辑重放，操作员选择性地标记和校正策略的行为，将监督集中在不确定性最高的地方，无需全程监控或单独的中断分类器。我们在三个灵巧操作任务（平底锅抓取放置、抽屉打开、阀门旋转）上使用10自由度XHand在标准和具有挑战性的初始配置下进行评估。主动标记在所有任务上持续优于行为克隆，提升高达23个百分点。与无指导的人机协作检查相比，VR-DAgger通过将审查集中在选定的片段而非完整部署上，将每个样本的收集时间减少了约40%。
英文摘要：
Learning from demonstrations is effective for robotic manipulation, but collecting sufficient task-specific data remains a major bottleneck. Under distribution shift, small errors compound, performance degrades, and expert time is often spent on redundant, low-value corrections instead of the few critical failure cases. We present VR-DAgger, a human-in-the-loop framework centered on an immersive VR application for dexterous teleoperation, demonstration collection, and selective policy correction. The VR client provides intuitive hand control with synchronized scene visualization, while a backend workstation runs simulation and learning, enabling autonomous rollouts without continuous operator oversight. We use Monte Carlo (MC) dropout to score uncertainty during Isaac Lab rollouts of a diffusion policy and select informative failure segments for correction. These segments are replayed in VR as clips, where the operator selectively labels and corrects the policy's behavior, concentrating supervision where uncertainty is highest without full-rollout monitoring or a separate intervention classifier. We evaluate on three dexterous manipulation tasks (Pan pick-and-place, Drawer opening, Valve turning) with a 10-DoF XHand under standard and challenging initial configurations. Active labeling consistently improves over behavioral cloning across all tasks, with gains of up to 23 percentage points. Compared to unguided human-in-the-loop inspection, VR-DAgger reduces per-sample collection time by approximately 40% by focusing review on selected segments rather than full rollouts.
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。
