Freeform preference learning lets robots take natural-language reward axes over binary feedback
chris_j_paxton · x · 2026-09-09
Specifying reward functions is one of the hardest parts of robot RL: binary success/failure labels and binary trajectory preferences are too sparse for long-horizon tasks.
Marcel Torne and collaborators propose freeform preference learning:
- Keeps preference learning's scalability (simple annotations, no complex reward function)
- Lets annotators define natural-language axes of preference for comparing trajectories
- Yields richer feedback signal for complex tasks
The team discussed the method on the RoboPapers podcast.
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