RL for Thinking in Robotics: Mastering Tasks in Under 100 Trials
svlevine · x · 2026-07-04
Sergey Levine's team introduces a method akin to "RL for thinking" in robotics—optimizing a robot's thought process through trial and error. Surprisingly, this approach is highly efficient, allowing the robot to learn in fewer than 100 real-world trials. Links to the project website and paper are included.
More from Research
- Alex Townsend posts 200 open problems in numerical linear algebra for humans and AI agents — IgorCarron · 2026-09-11
- Navier-Stokes, Riemann, P vs NP: what this week's math buzzwords mean for you — koltregaskes · 2026-09-11
- Fruit fly brain as an LLM: connectome-driven language model demo goes live — ngxson · 2026-09-11
- Harry Collins: LLMs can't do frontier science because they can't invent new language — whoamisri · 2026-09-11
- The Waymo effect: how AI is quietly making research less collaborative — JohnHammersley · 2026-09-11
- Causal-only attention for non-generative tasks is wasteful, argues HF engineer — antoine_chaffin · 2026-09-11