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.

翻译：人机协同（HitL）机器人部署作为一种半自主范式，已在学术界和工业界获得广泛关注。该范式允许人类操作者在部署阶段介入并调整机器人行为，从而提高任务成功率。然而，当需要部署大量机器人时，持续的人工监控与干预会带来极高的人力成本且难以实际应用。为应对这一局限，本文提出一种方法，使扩散策略仅在必要时主动寻求人工协助，从而降低对持续人工监督的依赖。为实现这一目标，我们利用扩散策略的生成过程计算基于不确定性的度量指标，使得自主智能体能够在部署时根据该指标决定是否请求操作者协助，且训练阶段完全无需操作者参与。此外，我们证明该方法同样适用于高效收集微调扩散策略所需的数据，从而提升其自主性能。仿真环境与真实场景的实验结果表明，我们的方法在多种情境下均能有效提升策略在部署阶段的性能表现。