GenieMaster: An Embodied Continual Harness for Persistent Multi-Robot Work

Persistent robot work requires general reasoning, responsive manipulation, and reliable recovery as tasks and environments change. Frontier agents provide high-level reasoning, while learned policies provide continuous, closed-loop control from current observations. We present GenieMaster, an embodied continual harness for persistent multi-robot work. At its core is the Embodied Execution Protocol (EEP), an interface through which the agent interacts with the physical world. Through EEP, the agent invokes robot capabilities and guides policies with spatial cues, object references, and detailed language. We co-design the guidance interface and policy training so that policies can translate agent-provided guidance into robot actions. The agent uses physical feedback as evidence for task progression, guidance revision, and recovery, while retained task state supports coordination across robots and resumption after interruptions. GenieMaster supports continual harness evolution through asynchronous reflection and memory consolidation, using execution experience to update guidance, assessment criteria, and orchestration strategies. Across six real-world tasks, GenieMaster achieves 78.67% macro-averaged success versus 38.83% for the Dual-system baseline based on a vision–language model. Further evaluations show improved retrieval of unseen products with object-reference conditioning, while a convenience-store deployment illustrates ongoing task orchestration over a 24-hour window as new requests arrive.