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Robots operating alongside humans often encounter unfamiliar environmentsthat make autonomous task completion challenging. Though improving models andincreasing dataset size can enhance a robot's performance in unseenenvironments, dataset generation and model refinement may be impractical inevery unfamiliar environment. Approaches that utilize human demonstrationthrough manual operation can aid in generalizing to these unfamiliarenvironments, but often require significant human effort and expertise toachieve satisfactory task performance. To address these challenges, we proposeleveraging part-time human interaction for redirection of robots during failedtask execution. We train a lightweight help policy that allows robots to learnwhen to proceed autonomously or request human assistance at times ofuncertainty. By incorporating part-time human intervention, robots recoverquickly from their mistakes. Our best performing policy yields a 20 percentincrease in path-length weighted success with only a 21 percent humaninteraction ratio. This approach provides a practical means for robots tointeract and learn from humans in real-world settings, facilitating effectivetask completion without the need for significant human intervention.

Learning When to Ask for Help: Transferring Human Knowledge through Part-Time Demonstration