Robots operating alongside humans often encounter unfamiliar environments that make autonomous task completion challenging. Though improving models and increasing dataset size can enhance a robot's performance in unseen environments, dataset generation and model refinement may be impractical in every unfamiliar environment. Approaches that utilize human demonstration through manual operation can aid in generalizing to these unfamiliar environments, but often require significant human effort and expertise to achieve satisfactory task performance. To address these challenges, we propose leveraging part-time human interaction for redirection of robots during failed task execution. We train a lightweight help policy that allows robots to learn when to proceed autonomously or request human assistance at times of uncertainty. By incorporating part-time human intervention, robots recover quickly from their mistakes. Our best performing policy yields a 20 percent increase in path-length weighted success with only a 21 percent human interaction ratio. This approach provides a practical means for robots to interact and learn from humans in real-world settings, facilitating effective task completion without the need for significant human intervention.

翻译：与人类协同作业的机器人在陌生环境中常面临自主任务完成的挑战。尽管改进模型和扩大数据集可提升机器人在未知环境中的性能，但在每个陌生环境中生成数据集并优化模型往往不切实际。通过手动操作利用人类演示的方法虽有助于机器人在这些陌生环境中泛化，但通常需要大量人力投入与专业技能才能实现令人满意的任务表现。为解决上述问题，我们提出在机器人任务执行失败时，利用非全时人类交互进行重定向的方法。通过训练轻量级求助策略，使机器人能够自主学习何时自主行动，或在不确定时刻请求人类协助。结合非全时人类干预，机器人可快速从错误中恢复。最优策略在仅需21%人类交互率的情况下，将路径长度加权成功率提升20%。该方法为机器人在真实场景中与人类交互学习提供了实用途径，在无需大量人类干预的前提下实现高效任务完成。