Robots can autonomously improve their policies by exploring corrections through reusable skills guided by foundation models, enabling scalable policy improvement without human demonstrations for each correction.
This paper presents skill-space shooting, a method that helps robots improve their policies by learning from their own failures without human demonstrations. The approach uses foundation models to guide exploration through reusable skills—short, familiar behaviors that can be composed to correct mistakes.