Freeform Preference Learning Lets Robot Annotators Compare Trajectories Along Natural-Language Axes
micoolcho · x · 2026-10-24
Defining reward functions is one of the hardest parts of robot RL: progress-style metrics are ill-defined and hard to estimate, so most robot learning falls back on sparse rewards or naive preference learning. But having human annotators pick one trajectory over another discards much of the signal in complex tasks and hurts learning efficiency.
On the RoboPapers podcast, Marcel Torne, Anubha Mahajan, and Abhijnya Bhat present their solution, Freeform Preference Learning: annotators can define natural-language axes along which to compare trajectories, preserving richer structured signal from preference data. The authors discuss the motivation and method details in the episode.
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