Uncertainty Comes for Free: Human-in-the-Loop Policies with Diffusion Models

2025-02-26

AI Summary

To address the high cognitive load on human operators and imprecise intervention timing in human-robot collaborative robot deployment, this paper proposes a diffusion-based uncertainty-aware framework. Methodologically, it leverages the intrinsic denoising trajectory variance of diffusion policies to directly quantify decision uncertainty—without requiring auxiliary training, labeled data, or human feedback. Based on this uncertainty estimate, we design a zero-shot autonomous intervention gating mechanism that triggers human assistance only when necessary. The framework integrates diffusion policy modeling, uncertainty estimation, an online human-robot interaction protocol, and efficient fine-tuning data collection. Evaluated in both simulation and real-world robotic tasks, our approach improves deployment success rates by 12–28% and reduces human intervention frequency by 67%, significantly enhancing system autonomy and collaborative efficiency.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Reduces need for constant human oversight in robot deployment
• Uses uncertainty metrics to request human assistance when necessary
• Improves autonomous performance through efficient data collection

Innovation

• Diffusion models reduce human oversight needs.
• Uncertainty metrics trigger human assistance requests.
• Method improves autonomous policy performance efficiently.