Robot Learning

Skill Chaining

Skill chaining is the composition of learned skills into longer behaviors by ensuring each skill terminates in a state from which the next skill can succeed — formally, within the next skill's initiation set. The idea originates in the options framework of hierarchical reinforcement learning (Konidaris and Barto, 2009) and reappears in long-horizon manipulation, where policies are trained or regularized so their terminal state distributions match successors' initial state distributions.

Why it matters for physical AI

Long-horizon tasks like cooking or assembly exceed what monolithic policies learn reliably; chaining verified skills with compatible boundary conditions is a scalable route to multi-step competence.