Ep#103: Freeform Preference Learning for Robotic Manipulation

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, which lets annotators define natural-language axes to compare trajectories over. This improves real-world performance on long-horizon manipulation tasks over sparse rewards and simple binary preference learning.

Watch Epsiode 103 of RoboPapers, with Michael Cho and Jiafei Duan, today to learn more!

Abstract

Reward design remains a central bottleneck for autonomous robot policy improvement, especially in long-horizon manipulation tasks where sparse success labels provide too little signal and binary preferences collapse many competing notions of quality into one ambiguous signal. We introduce Freeform Preference Learning (FPL), a method for learning robot policies from freeform human preferences. Rather than asking annotators which of two trajectories is better overall, FPL lets them define natural-language preference axes, such as speed, safety, quality of placement, or carefulness, and provide pairwise preferences along each axis. These annotations are used to learn a language-conditioned reward model that maps a trajectory and preference label to an axis-specific reward. We use this model to train a reward-conditioned policy that optimizes across the multiple human-specified dimensions. Across four real-world and two simulated long-horizon manipulation tasks, FPL improves over sparse-reward and binary-preference methods by 38 percentage points. Beyond improved performance, FPL learns dense progress signals without explicit subtask segmentation, shows compositionality of behavior not present in the data, and allows users to steer the policy towards different behaviors at test time without retraining. Blog post with videos available at this https URL

Learn More

Project page: https://freeform-pl.github.io/fpl.website/

ArXiV: https://arxiv.org/abs/2606.32027

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