Diffusion models can effectively assist human operators in robotic control by automating specific subtasks (like orientation), reducing cognitive load while maintaining human oversight—a practical model for human-AI collaboration in physical systems.
This paper presents HITL-D, a shared control system that combines diffusion-based AI policies with human input for robotic manipulation tasks. Instead of requiring operators to control every aspect of a robot arm, the system automatically handles orientation adjustments while the human focuses on positioning, reducing mental workload and task completion time by 40% in user studies.