让机器人在操作中听人指挥，用扩散模型提升协作效率。
HITL-D: Human In The Loop Diffusion Assisted Shared Control
- 用扩散模型结合人类控制，自动调整机械臂末端姿态。
- 任务时间减少40%，心理负担降低37%，体验更直观自信。
- 适合需要精细操作的远程控制场景，如手术或精密装配。
自主操控系统已取得显著进展，但将人类专家经验与基于扩散模型的策略在共享控制中结合仍较少被研究。本文提出人类在环路扩散（HITL-D）框架，通过融合扩散策略与人类控制，实现多步骤插入和精细操作任务中对机械臂末端姿态的自主更新，该更新基于场景点云和末端执行器的笛卡尔位置。此方法减少了操纵杆控制轴数，从而降低认知负荷。12名参与者参与的多任务实验表明，相比传统遥操作，HITL-D平均任务完成时间缩短40%，感知工作量下降37%，且在独立性、直观性和信心等李克特量表评分上均有提升。结果表明，HITL-D有效整合了人类专长与自主辅助，全面改善了遥操作的客观与主观表现。
原文摘要 · Abstract (English)
Autonomous manipulation systems have achieved remarkable capabilities, yet the integration of human expertise with diffusion-based policies in shared control remains relatively unexplored. In this paper, we propose Human-In-The-Loop Diffusion (HITL-D), a shared control framework that enhances user performance in multi-step, insertion, and fine manipulation tasks. HITL-D leverages a novel combination of diffusion-based policies and human control to provide autonomous end effector orientation updates conditioned on a scene point cloud and the Cartesian position of the end effector. This approach reduces the number of joystick control axes required, thereby lowering mental workload. In a multi-task user study with 12 participants, HITL-D reduced average task completion times by 40%, decreased perceived workload by 37%, and improved Likert-scale ratings for independence, intuitiveness, and confidence compared to traditional teleoperation methods. These results demonstrate that HITL-D effectively integrates human expertise with autonomous assistance, improving both objective and subjective aspects of teleoperation.
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