Ep#111: SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning

Podcast: RoboPapers · Chris Paxton and Michael Cho

Opublikowano: 2026-10-09

Reinforcement learning is often seen as key to achieving scalable, highly reliable real-world robot performance, yet it requires good reward functions that can supervise the wide range of tasks that need to be performed. The obvious thing to try would be a large vision-language model (VLM), but these models often fail under common real-world conditions like partial observability and occlusion. As a solution Philip Schroeder and Thomas Weng propose SOLE-R1, a video-language reasoning model trained to serve as a reward signal for online RL. Given real video, SOLE-R1 will reason over input video frames, generates reasoning traces...