Rethinking the Implications of Human Feedback for Preference Learning in Human-Robot Collaboration

The paper critiques the standard fixed-rule approach for deriving labels from human feedback in human-robot collaboration, showing that human-provided implication labels often differ and improve reward learning. It introduces IMPLIED, a method that starts with fixed-rule implications but learns to infer and revise accepted/rejected action labels over time, outperforming both the fixed rule and LLM baselines on recorded trajectories and a physical pizza‑making study. As a result, IMPLIED reduces preference‑estimation error and yields robot actions that better align with combined reward objectives.

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• llms
• reinforcement-learning
• robotics