Evolving Programmatic Skill Networks
We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of executable skills. We introduce the Programmatic Skill Network (PSN), a framework in which skills are executable symbolic programs forming a compositional network that evolves through experience. PSN defines three core mechanisms instantiated via large language models: (1)REFLECT for structured fault localization over skill compositions, (2) progressive optimization with maturity-aware update gating that stabilizes reliable skills while maintaining plasticity for uncertain ones, and (3) canonical structural refactoring under rollback validation that maintains network compactness. We further show that PSN's learning dynamics exhibit structural parallels to neural network training. Experiments on MineDojo and Crafter demonstrate robust skill reuse, rapid adaptation, and strong generalization across open-ended task distributions.\footnote{We plan to open-source the code.
695fbc7d5b7998385e639349
Haochen Shi
claimed_verified
2026-01-09T08:35:44.276Z
620d5b00d6db8f314af2dd5f
/avatars/cc0329170fa1ef68e98893de2066c92d.svg
HAOCHEN SHI
hcshi
user
hcshi
695fbc7d5b7998385e63934a
Xingdi Yuan
695fbc7d5b7998385e63934b
Bang Liu
Evolving Programmatic Skill Networks
We study continual skill acquisition in open-ended embodied environments where an agent must construct, refine, and reuse an expanding library of executable skills. We introduce the Programmatic Skill Network (PSN), a framework in which skills are executable symbolic programs forming a compositional network that evolves through experience. PSN defines three core mechanisms instantiated via large language models: (1)REFLECT for structured fault localization over skill compositions, (2) progressive optimization with maturity-aware update gating that stabilizes reliable skills while maintaining plasticity for uncertain ones, and (3) canonical structural refactoring under rollback validation that maintains network compactness. We further show that PSN's learning dynamics exhibit structural parallels to neural network training. Experiments on MineDojo and Crafter demonstrate robust skill reuse, rapid adaptation, and strong generalization across open-ended task distributions.\footnote{We plan to open-source the code.
Programmatic Skill Network enables continual skill acquisition through executable symbolic programs that evolve via reflection, progressive optimization, and structural refactoring mechanisms.
Programmatic Skill Network executable symbolic programs skill composition structured fault localization progressive optimization maturity-aware update gating canonical structural refactoring rollback validation neural network training skill reuse rapid adaptation generalization