Rethinking the Implications of Human Feedback for Preference Learning in Human-Robot Collaboration
In Human-Robot Interaction, the standard approach to learn a reward model that represents human preferences for robot behavior consists of three steps. First, the robot collects limited direct evidence from human feedback (e.g., positive or negative binary feedback). Then, the robot utilizes the direct evidence to derive accepted or rejected labels to feasible but unchosen actions using fixed implication rules. Finally, the robot updates the reward model with both the direct and derived evidence. Unfortunately, the fixed rule can hinder preference learning: in a user study with two collaborative simulation environments, human-provided implication labels often differed from the standard fi...
| Authors | Qiping Zhang, Kate Candon, Debasmita Ghose et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 September 2026 |
| Last updated at source | 12 September 2026 |
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