Advancing Omnimodal Embodied Agents from Isolated Skills to Everyday Physical Autonomy
This work addresses the challenge of persistent autonomy in unstructured environments for embodied agents, unifying cyber-physical actions and enabling autonomous failure recovery.
OmniAct introduces a hierarchical asynchronous architecture for persistent embodied agents, integrating a multimodal planner, adaptive memory, and visual preemption engine. In 40 real-world tasks across two robotic platforms and four IoT devices, it achieves consistent success rate improvements, near-flat token consumption under 100k+ tokens, and elevates mid-scale open-weight models to proprietary-level performance.
Building persistent embodied agents in unstructured environments demands unified orchestration of heterogeneous tools spanning both cyber (APIs, IoT) and physical (manipulation, navigation) domains, coupled with autonomous recovery from physical failures that inevitably arise over extended operation. Existing systems treat these as separate problems: VLM-based planners lack a unified cyber-physical action space, agent frameworks accumulate unbounded context that degrades temporal coherence, and VLA policies execute open-loop without detecting their own failures. We argue that persistent autonomy requires not a monolithic model but a hierarchical asynchronous architecture with explicit separation of planning, memory, and verification. To this end, we present OmniAct, a framework integrating a multimodal semantic planner for skill routing across unified action spaces, an adaptive hierarchical memory with event-boundary-driven compression for sub-linear context growth, and an asynchronous visual preemption engine that closes the semantic loop during physical execution. Across 40 real-world long-horizon tasks on two robotic platforms coordinating four IoT devices, OmniAct achieves consistent improvements in end-to-end success across all complexity levels, maintains near-flat token consumption over under 100k+ accumulated interaction tokens, and elevates mid-scale open-weight models to proprietary-level performance.