HoloAgent-0: A Unified Embodied Agent Framework with 3D Spatial Memory
This work addresses the challenge of unifying specialized embodied AI modules into a coherent system for real-world robot deployment, but the evaluation is preliminary and lacks quantitative comparisons to baselines.
HoloAgent-0 introduces a unified framework for real-world robot deployment that integrates closed-loop execution, 3D spatial memory, and embodied skills. Evaluated on real hardware, it demonstrates capabilities in spatial memory, long-horizon navigation, and closed-loop execution across multiple tasks.
LLM agents follow a practical execution loop in digital environments: they reason over structured states, invoke tools, inspect feedback, and revise actions. Extending this loop to physical robots is difficult because physical execution is continuous, embodiment-dependent, uncertain, and constrained by safety. Existing embodied-AI systems have advanced manipulation, spatial understanding, navigation, and humanoid control, but these capabilities often remain specialized modules or loosely coupled decision loops. In this work, we introduce HoloAgent-0, a unified embodied agent framework for real-world robot deployment. Embodied AgentOS converts language instructions into executable skill graphs, schedules robot resources, monitors execution, and triggers clarification or re-planning from runtime feedback. HoloAgent-0 organizes heterogeneous robot models and controllers through three coupled layers: Embodied AgentOS for closed-loop execution, 3D spatial memory for physical world grounding, and embodied skills for robot action. We deploy HoloAgent-0 on real hardware and evaluate its spatial memory, long-horizon navigation, and closed-loop execution across motion generation, object search, cross-robot coordination, and mobile manipulation.