AffordCode: Learning Generalizable Affordances as Executable Programs

Robotic affordance learning treats affordances as prediction outputs, including interaction regions, poses, or trajectories. Advances in language models and programmatic object modeling enable inspectable, editable, and composable affordance programs, whose potential for feedback-driven refinement and cross-instance reuse remains underexplored.To bridge this gap, we present AffordCode, an agentic framework that learns reusable affordance programs over parameterized object templates exposing part geometry and composition. Following inter-part structural relationships, AffordCode constructs functional chains bottom-up, composing part-level functions into object-level programs that compute instance-specific affordances. A closed-loop procedure verifies candidate programs, traces structural, geometric, and interaction feedback to faulty assumptions and dependencies, and refines the corresponding code. This method offers three advantages: (1) Precise Feedback-Driven Refinement where editable code exposes faulty interaction logic; (2) Coherent Object-Level Behavior since bottom-up composition preserves part dependencies and aligns local functions with the task; and (3) Synthesis-Free Reuse: validated part functions can be recomposed and object programs instantiated across compatible objects. Extensive experiments demonstrate the effectiveness of the generated affordances, their generalization across object instances, and their utility in downstream tasks.