Real-Time Operator Takeover for Visuomotor Diffusion Policy Training

2025-02-04

AI Summary

Vision-motion diffusion policies struggle with out-of-distribution (OOD) states in real-world settings and lack mechanisms for real-time human intervention. Method: This paper introduces the Real-Time Operator Takeover (RTOT) paradigm, featuring (1) a novel online anomaly detection mechanism based on Mahalanobis distance for precise OOD state identification, and (2) a takeover-demonstration-driven incremental policy training framework that directly incorporates human takeover actions into the policy optimization loop. Contribution/Results: RTOT significantly improves policy generalization and robustness. Under equivalent data budgets, it achieves substantially higher task success rates than conventional long-horizon imitation learning. Real-robot rice-scooping experiments validate its capabilities in critical failure point detection, sub-second recovery, and seamless human–robot collaborative control.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

We present a Real-Time Operator Takeover (RTOT) paradigm enabling operators to seamlessly take control of a live visuomotor diffusion policy, guiding the system back into desirable states or reinforcing specific demonstrations. We presents new insights in using the Mahalonobis distance to automaicaly identify undesirable states. Once the operator has intervened and redirected the system, the control is seamlessly returned to the policy, which resumes generating actions until further intervention is required. We demonstrate that incorporating the targeted takeover demonstrations significantly improves policy performance compared to training solely with an equivalent number of, but longer, initial demonstrations. We provide an in-depth analysis of using the Mahalanobis distance to detect out-of-distribution states, illustrating its utility for identifying critical failure points during execution. Supporting materials, including videos of initial and takeover demonstrations and all rice-scooping experiments, are available on the project website: https://operator-takeover.github.io/

Problem

• Real-time operator takeover for visuomotor diffusion policy training
• Using Mahalanobis distance to identify undesirable states
• Improving policy performance with targeted takeover demonstrations

Innovation

• Real-Time Operator Takeover system
• Mahalanobis distance for state detection
• Seamless control transition policy