发表机构
Astribot Team（Astribot团队）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究与智能机器人长期物理共存问题，提出基于机器人网关抽象的多机器人代理PHILIA，解耦代理推理与机器人执行，实现即插即用集成，在Astribot S1机器人上验证架构，展示用例，强调人机交互流程对有效协助的重要性。
AI 中文摘要
与智能机器人的长期物理共存需要的不仅仅是有效的机器人策略。一个持久的机器人助手必须支持多样化的用户界面，保持对人和偏好的长期记忆，跨机器人实体进行协调，并将人类意图转化为安全的物理执行。我们引入了PHILIA，一个围绕机器人网关抽象构建的多机器人代理。PHILIA保留了OpenClaw丰富的交互和工具生态系统，同时通过统一的能力接口暴露机器人本地运行时、机载感知、导航、扬声器和机器人策略。这种设计将低频、高语义的代理推理与高频、低级的机器人执行解耦，实现了用户界面、机器人实体和策略后端的即插即用集成。我们在Astribot S1机器人上验证了该架构，同时设计机器人网关合同以通过共享能力接口支持未来的异构机器人平台。我们展示了代表性用例，其中代理记忆和场景理解基于机器人动作。这些涵盖了交互式家庭场景，从简单的整理到具有挑战性的长期和灵巧的服务任务，如打包背包和提起垃圾袋。我们强调了人机交互流程，其中对用户意图和偏好的上下文理解，以及执行过程中的人工确认或调整，对于有效协助至关重要。
英文摘要
Long-term physical coexistence with intelligent robots requires more than capable robot policies. A persistent robotic assistant must support diverse user-facing interfaces, maintain long-horizon memory of people and preferences, coordinate across robot embodiments, and translate human intent into safe physical execution. We introduce PHILIA, a multi-robot agent built around a robot gateway abstraction. PHILIA retains the rich interaction and tool ecosystem of OpenClaw while exposing robot-local runtimes, onboard perception, navigation, speaker, and robot policies through a unified capability interface. This design decouples low-frequency, high-semantic agent reasoning from high-frequency, low-level robot execution, enabling plug-and-play integration of user interfaces, robot embodiments, and policy backends. As a result, the user experience becomes compositional: advances in user interfaces, robot embodiments, robot policies, navigation, or interaction algorithms can improve the overall experience without redesigning the system. We validate the architecture on Astribot S1 robots while designing the robot gateway contract to support future heterogeneous robot platforms through a shared capability interface for observation, task execution, navigation, speech playback, status monitoring, and task cancellation. We present representative use cases in which agent memory and scene understanding are grounded in robot actions. These span interactive household scenarios, ranging from simple organization to challenging long-horizon and dexterous service tasks, such as packing a backpack and lifting a garbage bag. We highlight the human-robot interaction flow, where contextual understanding of user intent and preferences, together with human-in-the-loop confirmation or adjustment during execution, is essential for effective assistance.
