扩散模型自动生成求助信号，减少人工干预负担。
Uncertainty Comes for Free: Human-in-the-Loop Policies with Diffusion Models
- 利用扩散模型生成过程计算不确定性，决定是否请求帮助。
- 实测表明部署性能提升，且无需训练时人工参与。
- 适合需要高效人机协作的机器人应用场景。
人机协同（HitL）机器人部署在学术界和工业界日益受到关注，作为一种半自主范式，允许操作员在部署时干预并调整机器人行为，从而提高成功率。然而，持续的人工监控与干预在大规模部署机器人时可能过于耗时且不切实际。为解决这一问题，我们提出一种方法，使扩散策略仅在必要时主动寻求人工协助，从而减少对持续人工监督的依赖。为此，我们利用扩散策略的生成过程，计算基于不确定性的度量，使自主代理在部署时可据此决定是否请求操作员协助，而无需训练阶段的操作员互动。此外，我们还证明该方法可用于高效数据收集，以微调扩散策略，提升其自主性能。模拟和真实环境中的实验结果表明，该方法在多种场景下均能提升部署期间的策略表现。
原文摘要 · Abstract (English)
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.
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