Uncertainty Comes for Free: Human-in-the-Loop Policies with Diffusion Models
This addresses the challenge of scaling semi-autonomous robots by reducing constant human oversight, though it is incremental as it builds on existing diffusion policy methods.
The paper tackles the problem of reducing labor-intensive human monitoring in human-in-the-loop robot deployment by proposing a method that uses diffusion policies to compute uncertainty and request human assistance only when necessary, improving policy performance in simulated and real-world scenarios.
Human-in-the-loop (HitL) robot deployment has gained significant attention in both academia and industry as a semi-autonomous paradigm that enables human operators to intervene and adjust robot behaviors at deployment time, improving success rates. However, continuous human monitoring and intervention can be highly labor-intensive and impractical when deploying a large number of robots. To address this limitation, we propose a method that allows diffusion policies to actively seek human assistance only when necessary, reducing reliance on constant human oversight. To achieve this, we leverage the generative process of diffusion policies to compute an uncertainty-based metric based on which the autonomous agent can decide to request operator assistance at deployment time, without requiring any operator interaction during training. Additionally, we show that the same method can be used for efficient data collection for fine-tuning diffusion policies in order to improve their autonomous performance. Experimental results from simulated and real-world environments demonstrate that our approach enhances policy performance during deployment for a variety of scenarios.