arXiv:2605.21460v1 Announce Type: cross Abstract: 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 Car
The proliferation of diffusion models in AI combined with the increasing demand for more dexterous and reliable autonomous manipulation systems is driving innovation in human-robot collaboration.
Improving shared control for complex manipulation tasks could significantly accelerate the deployment of advanced robotics in manufacturing, logistics, and skilled service industries by enhancing reliability and user confidence.
The explicit integration of human expertise with advanced AI policies like diffusion models creates a new paradigm for human-in-the-loop robotic control, moving beyond simple teleoperation or fully autonomous systems.
- · Robotics companies
- · Manufacturing sector
- · Logistics and warehousing
- · AI software developers
- · Tasks requiring repetitive, precise manual labor
- · Purely teleoperated systems
- · Traditional industrial automation
Enhanced robotic systems capable of performing more delicate and complex tasks with human oversight become viable.
Increased adoption of such systems could lead to a re-evaluation of labor roles, focusing more on supervisory and problem-solving functions rather than direct manipulation.
The success of human-in-the-loop diffusion in manipulation could inspire similar shared control frameworks in other complex, safety-critical AI applications, accelerating general AI adoption.
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Read at arXiv cs.AI