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papersTODAY 04:00 UTC

arXiv Paper Reexamines Human Feedback for Robot Preference Learning

A new arXiv paper examines how robots typically build reward models of human preferences, a process that starts with collecting limited direct feedback such as positive or negative signals. The authors argue that the assumptions behind this standard three-step pipeline deserve reconsideration in the context of human-robot collaboration. The work is cross-listed in the cs.AI category.

arXiv cs.AI human-feedback human-robot collaboration reward modeling robot preference learning

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arXiv cs.AIRethinking the Implications of Human Feedback for Preference Learning in Human-Robot Collaboration ↗TODAY 04:00 UTC