Evolving Programmatic Skill Networks

The article introduces the Programmatic Skill Network (PSN), a framework for continual skill acquisition in embodied environments that utilizes large language models to create executable symbolic programs. Key mechanisms include structured fault localization, maturity-aware optimization, and canonical structural refactoring, which enhance skill stability and adaptability. Experiments conducted in MineDojo and Crafter show that PSN achieves effective skill reuse and generalization, highlighting its potential for advancing AI agents in dynamic task environments.