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On the Effect of Robot Errors on Human Teaching Dynamics

arXiv 2409.09827 ↗First posted 15 Sep 2024cs.RO cs.AI cs.HCcited by 0

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Abstract (arXiv)

Human-in-the-loop learning is gaining popularity, particularly in the field of robotics, because it leverages human knowledge about real-world tasks to facilitate agent learning. When people instruct robots, they naturally adapt their teaching behavior in response to changes in robot performance. While current research predominantly focuses on integrating human teaching dynamics from an algorithmic perspective, understanding these dynamics from a human-centered standpoint is an under-explored, yet fundamental problem. Addressing this issue will enhance both robot learning and user experience. Therefore, this paper explores one potential factor contributing to the dynamic nature of human teaching: robot errors. We conducted a user study to investigate how the presence and severity of robot errors affect three dimensions of human teaching dynamics: feedback granularity, feedback richness, and teaching time, in both forced-choice and open-ended teaching contexts. The results show that people tend to spend more time teaching robots with errors, provide more detailed feedback over specific segments of a robot's trajectory, and that robot error can influence a teacher's choice of feedback modality. Our findings offer valuable insights for designing effective interfaces for interactive learning and optimizing algorithms to better understand human intentions.

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References · 13

  1. 2017Deep reinforcement learning from human preferencescited by 3,846
  2. 2023Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedbackcited by 743
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  7. 2022The Effect of Modeling Human Rationality Level on Learning Rewards from Multiple Feedback Typescited by 26
  8. 2022Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learningcited by 22
  9. 2024Uni-RLHF: Universal Platform and Benchmark Suite for Reinforcement Learning with Diverse Human Feedbackcited by 20
  10. 2020FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedbackcited by 15
  11. 2023RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedbackcited by 15
  12. 2018Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learningcited by 9
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  1. 2019Improving User Specifications for Robot Behavior through Active Preference Learning: Framework and Evaluationcited by 7

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