Learning to Compose: Recursive Self-Improvement of Shared Executable Stacks for Zero-Shot Long-Horizon Robot Manipulation

Abstract:Individually competent robot skills can fail when composed: tools may lack a required capability, completion signals may misrepresent physical progress, and one skill may leave a state that violates its successor's prerequisites. We introduce Recursia, a recursive self-improvement framework that treats the shared executable stack, rather than individual task programs, as the persistent unit of improvement. The stack integrates tools, skills with execution contracts, handoffs, and verifiers. Source-side execution traces guide failure attribution and the generation of missing tools or repairs to existing shared components. Generality checks, mechanism tests, and paired selection and regression gates determine which changes become the next parent. The resulting stack is frozen and reused through declarative task adapters, allowing source-derived changes to support new compositions without target-side code repair. Experiments cover LIBERO-Pro Long simulation and a physical Franka FR3 equipped with a Franka Hand. In simulation, Recursia improves success over ASPIRE by 5.8 percentage points under Pos perturbations and 15.1 points under Task perturbations. Across three physical compositional target tasks, it improves mean success over the Skill Document Only baseline by 40 percentage points. Analyses of constituent execution, candidate rejection, and inference drivers distinguish local mechanism improvements from complete task success and characterize the cost of feedback-conditioned orchestration. These results examine shared executable interfaces as a concrete mechanism for compositional transfer in robot programming.

Artificial Intelligence (cs.AI)

arXiv:2610.24464 [cs.AI]

(or arXiv:2610.24464v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.24464 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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