Article

FERRET: Using Environment Edits as a Corrective Signal for Reward Learning

The authors propose environment edits as a complementary form of feedback for robot corrections: with the robot's motion held constant, a human edits the scene so that the only explanation for the edit is the moved object. They introduce FERRET (Feature Learning from Environmental and Robot-Relational Edit Traces), which learns preferences from both robot corrections and environment corrections, and they show that environment edits can disambiguate features and learn preferences that robot corrections alone cannot.

Part of the event: FERRET Learns Robot Preferences Using Environment Edits

This page provides a summary and source link only.