Cheng, Yi, Fang and 12 co-authors · In-Context Robot Learning

GPT-Policy

In-context robot learning with a fixed general-purpose vision-language model as the acting agent, named on the page as GPT-6 Astra, with no gradient updates or parameter changes between tasks. A context compiler feeds the frozen model, which proposes robot-tool actions, and a constrained controller validates and executes them, drawing on human video demonstrations, robot demonstrations with actions, target images, self-interaction history and human-robot interaction. Across 10 tasks in 5 experiment families it reports 2 of 3 successes on Pick Red Towel with human video against 0 of 3 without, and 3 of 3 on Unscrew Bottle Cap with richer context. Worth noting the sample size: three trials per condition, which the page does not caveat. Code published, licence not stated, institutional affiliation not given.