ASPIRE

ASPIRE reframes robot programming as continual, code-as-policy learning that compounds experience instead of discarding it. The system runs an open-ended loop with a closed-loop execution engine that exposes fine-grained multimodal traces, a skill library that distills validated fixes into transferable knowledge, and an evolutionary search over task sequences and control programs. It surpasses prior methods by up to 77% on perturbed manipulation and enables zero-shot generalization to unseen long-horizon tasks, with early evidence of sim-to-real transfer across different embodiments. ---