用智能代理框架自动调试双臂机器人，降低部署门槛。
SPINE: Bridging the Cyber-Physical Gap with Agentic AI
- 构建上下文与诊断修复的双代理流程，自动完成机器人调试。
- 新手使用后操作成功率达100%，平均调试时间缩短至13分47秒。
- 跨平台适用，显著减少对专家经验依赖，适合机器人初学者使用。
基础模型为机器人提供了复杂决策能力，但将其部署到物理平台仍需大量耗时且依赖专家的调校。这一部署瓶颈——机器人的‘脊柱’——是实现可扩展具身AI的主要障碍。为此，我们提出SPINE（基于智能体的可扩展物理集成）：一个系统化调试与部署双臂机器人的智能体框架，仅需极少机器人知识。SPINE包含两个协同的多智能体工作流：配置生成器创建机器人专属上下文，调试器循环执行诊断、修复与验证直至远程操控成功。在七个DOBOT X-Trainer调试场景中，无经验用户使用SPINE的表现优于使用Claude Code的人类操作员，操作成功率从75%提升至100%，平均调试时间由16分45秒降至13分47秒。在另一款异构的AgileX PiPER（ROS/CAN双臂）上，SPINE成功解决全部10个预设缺陷，而专家基线仅解决9个，耗时相近。结果表明，SPINE具备跨平台迁移能力，能显著降低对专家调校的依赖，推动具身AI向规模化真实应用迈进。
原文摘要 · Abstract (English)
Foundation models have given robots a sophisticated brain for complex decision-making, yet deploying that intelligence into a physical platform still demands tedious, expert-driven calibration. This deployment gap, the robot's spinal cord, remains a primary bottleneck to scalable Embodied AI. Hence, we propose SPINE (Scalable Physical Integration with ageNtic Expertise): an agentic framework for systematically debugging and deploying bimanual robots with minimal robotics expertise. SPINE's harness comprises two orchestrated multi-agent workflows: a profile builder that creates robot-specific context, and a debugger that cycles through diagnosis, repair, and validation until teleoperation works. Across seven DOBOT X-Trainer debugging scenarios, a robotics novice using SPINE outperformed human operators using Claude Code with the same reference materials, but without SPINE's structured workflow, improving operationalization success from 75% to 100% and reducing mean time-to-teleoperation from 16 min 45 s to 13 min 47 s. On AgileX PiPER, a distinct ROS/CAN bimanual arm, SPINE resolved all 10 implanted bugs, versus 9 out of 10 for the expert baseline, in nearly the same amount of time. Together, these results show that SPINE can transfer across bimanual platforms, reduce dependence on expert calibration, and move embodied AI closer to scalable real-world deployment.
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