Ep#103: Freeform Preference Learning for Robotic Manipulation

Podcast: RoboPapers · Chris Paxton and Michael Cho

Opublikowano: 2026-09-09

Specifying reward functions for robots is one of the hardest things about reinforcement learning. Robot rewards often need to be very detailed; metrics like progress can be ill-defined and hard to estimate. This leads to most robot learning defaulting to sparse rewards or simple preference learning. But naive preference learning (having human annotators choose one trajectory over another) is an easy solution, but obscures a lot of the signal in complex tasks and can make learning a lot less efficient. Marcel Torné, Anubha Mahajan, and Abhijnya Bhat join us to talk about their solution: freeform preference learning, w...