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：与人类协同工作的机器人常面临不熟悉的环境，这使得自主完成任务具有挑战性。尽管改进模型和增加数据集规模能提升机器人在未知环境中的表现，但在每个陌生环境中生成数据集和优化模型可能并不现实。通过手动操作利用人类演示的方法有助于适应这些不熟悉的环境，但往往需要大量人类努力和专业知识才能达到令人满意的任务性能。为解决这些挑战，我们提出利用兼职人类交互在任务执行失败时对机器人进行重新引导。我们训练一个轻量级帮助策略，使机器人学会在不确定时自主前进或请求人类援助。通过引入兼职人类干预，机器人能从错误中快速恢复。我们表现最佳的策略仅在21%的人类交互比率下，实现了路径长度加权成功率20%的提升。该方法为机器人在现实场景中与人交互并学习提供了实用途径，在不需大量人类干预的情况下促进任务有效完成。