Shared autonomy imitation learning, in which robots share workspace with humans for learning, enables correct actions in unvisited states and the effective resolution of compounding errors through expert's corrections. However, it demands continuous human attention and supervision to lead the demonstrations, without considering the risks associated with human judgment errors and delayed interventions. This can potentially lead to high levels of fatigue for the demonstrator and the additional errors. In this work, we propose an uncertainty-aware shared autonomy system that enables the robot to infer conservative task skills considering environmental uncertainties and learning from expert demonstrations and corrections. To enhance generalization and scalability, we introduce a hierarchical structure-based skill uncertainty inference framework operating at more abstract levels. We apply this to robot motion to promote a more stable interaction. Although shared autonomy systems have demonstrated high-level results in recent research and play a critical role, specific system design details have remained elusive. This paper provides a detailed design proposal for a shared autonomy system considering various robot configurations. Furthermore, we experimentally demonstrate the system's capability to learn operational skills, even in dynamic environments with interference, through pouring and pick-and-place tasks. Our code will be released soon.

翻译：共享自主模仿学习允许机器人与人类共享工作空间进行学习，在未访问状态下执行正确动作，并借助专家修正有效解决累积误差。然而，该方法需要人类持续关注和监督以引导示范过程，未考虑人为判断错误及干预延迟带来的风险，这可能导致示范者高度疲劳并引入额外错误。本文提出一种考虑不确定性的共享自主系统，使机器人能够在考虑环境不确定性的情况下推断保守任务技能，并通过专家示范与修正进行学习。为提升泛化性与可扩展性，我们引入基于分层结构的技能不确定性推理框架，在更高抽象层级运行。将该框架应用于机器人运动控制以促进更稳定的交互。尽管共享自主系统在近期研究中展现出高水平成果并发挥关键作用，但具体系统设计细节仍不明晰。本文针对不同机器人配置提出详细的共享自主系统设计方案。通过倾倒与拾取放置任务，我们实验验证了系统在动态干扰环境下学习操作技能的能力。相关代码将尽快开源。