OmniAct: A Framework for Long-Horizon Cyber-Physical Embodied Agents

Embodied agents deployed in everyday environments must carry out extended tasks across physical and digital domains while respecting user requirements and responding to unexpected execution outcomes. This requires coordinating robotic skills and digital tools with context management and timely feedback. We present **OmniAct**, a framework for long-horizon cyber-physical embodied agents that integrates multimodal planning, hierarchical memory, and asynchronous visual monitoring. A shared skill interface supports task decomposition and execution across heterogeneous tools and robotic policies. Sensory, episodic, and reflective memory organize recent observations, task events, and reusable experience, while visual monitoring enables interruption and replanning during physical execution. Constraints derived from earlier user dialogue are retained in reflective memory and supplied to the monitor, allowing historical requirements to guide stopping decisions. Across 40 manipulation and navigation tasks on two robotic platforms, OmniAct achieves overall end-to-end success rates of 63.3% and 65.0%, respectively, exceeding the evaluated planning, agent-harness, and composed baselines. Ablations support the contributions of memory organization and timely intervention. Further evaluations show improved constraint satisfaction across six planner backbones and substantially reduced input-context growth over 270 interaction rounds. These results demonstrate the value of coordinating memory and execution feedback for long-horizon embodied tasks.