SPINE：用智能体人工智能弥合网络物理差距

中文摘要 AI 辅助

基础模型为机器人赋予了用于复杂决策的精密大脑，但将这种智能部署到物理平台仍需要繁琐的、专家驱动的校准。这种部署差距，即机器人的脊髓，仍然是可扩展的具身人工智能的主要瓶颈。因此，我们提出了SPINE（具有智能专家的可扩展物理集成）：一个用于以最少的机器人专业知识系统地调试和部署双臂机器人的智能体框架。SPINE的框架包括两个精心编排的多智能体工作流程：一个创建特定于机器人的上下文的配置文件构建器，以及一个循环进行诊断、修复和验证直到遥操作成功完成的调试器。在七个DOBOT X-Trainer调试场景中，使用SPINE的机器人新手在使用相同参考材料的情况下，其表现优于使用Claude Code的人类操作员，但没有SPINE的结构化工作流程，将操作成功率从75%提高到100%，并将平均遥操作时间从16分45秒减少到13分47秒。在AgileX PiPER（一个不同的ROS/CAN双臂机器人）上，SPINE解决了所有10个植入的错误，而专家基线解决了10个中的9个，且时间几乎相同。这些结果共同表明，SPINE可以跨双臂平台转移，减少对专家校准的依赖，并使具身人工智能更接近可扩展的实际部署。

英文摘要

Foundation models give robots powerful high-level reasoning, yet turning that intelligence into reliable physical action remains difficult: roboticists must still align device drivers, network interfaces, sensors, controllers, and safety constraints for each platform. This often-overlooked integration layer acts as the robot's spinal cord, translating high-level intent into coordinated physical behavior, and remains a primary bottleneck for scalable Embodied AI. Hence, we propose SPINE (Scalable Physical Integration with ageNtic Expertise), an agentic framework for systematically debugging and deploying bimanual robots for teleoperation. SPINE centers on two subagent-driven workflows: a profile builder that compiles robot-specific context and a debugger that uses that context to iterate through diagnosis, repair, and validation until teleoperation succeeds. Across two bimanual robot platforms and 12 debugging scenarios, novice-operated SPINE achieved more complete and efficient recovery than human operators using Claude Code. On DOBOT X-Trainer, SPINE improved success from 76% to 100% and reduced mean time-to-teleoperation by 30\%; on AgileX PiPER, SPINE also achieved 100\% success and reduced mean time-to-teleoperation by 38%. These results show that structured agentic debugging can address a key cyber-physical integration bottleneck in real-world robot deployment.

发表机构

• Northwestern University（西北大学）

机构由 AI 辅助整理，请以论文原文为准。