Refining Programmatic Runtime Policies for Efficient Contingency Adaptation

Robotic systems operating in dynamic environments require continual adaptation of coordination among reusable perception and control capabilities in response to concurrent events during execution. By bringing general-purpose programming abstractions to physical execution, programmatic policy frameworks provide an executable layer for orchestrating these capabilities into behavior, with an explicit organization that can be reconfigured using execution feedback. However, contingencies can alter runtime coordination among concurrently executing skills, while coordination embedded within task logic makes the refinement target ambiguous between skill behavior and coordination and leads to repeated recovery and validation across task compositions. We propose RaP, a *compile–execute–refine* framework that externalizes runtime coordination from generated task programs as a persistent refinement target and represents uncertainty over skill behavior and coordination as hypotheses. At *compile* time, RaP converts skill invocations into runtime-managed processes and augments the task program with conditional tests of these hypotheses. During *execution*, the runtime enforces coordination relations and records hypothesis-test outcomes, while *refinement* uses this evidence to target revisions to skill behavior or coordination conditions and distills validated repairs into reusable guidance with explicit applicability conditions. On long-horizon multi-arm manipulation tasks under concurrent events that alter coordination requirements, RaP achieves higher mean held-out success than task-program repair after three refinement rounds.