做机器人遥操作或人机协作研究的团队，HITL-D 用扩散模型把操作者的认知负担砍掉近四成，值得在精细操作场景里试试。
HITL-D 是一种结合人类操作与扩散模型的新型共享控制框架，专门针对多步骤、插入和精细操作任务。它通过场景点云和末端执行器笛卡尔位置，自主更新末端执行器方向，减少操纵杆控制轴数，降低操作者认知负荷。12 人用户研究表明，相比传统遥操作，HITL-D 将任务完成时间平均缩短 40%，感知工作负荷降低 37%，并在独立性、直观性和信心等主观评分上显著提升。该工作首次将扩散策略引入人机共享控制，为复杂操作任务的人机协作提供了新范式。
HITL-D: Human In The Loop Diffusion Assisted Shared Control
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.