SPINE: Bridging the Cyber-Physical Gap with Agentic AI

SPINE：用智能体 AI 弥合网络与物理世界的鸿沟

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.

基础模型为机器人提供了用于复杂决策的“大脑”，然而将这种智能部署到物理平台中，仍然需要繁琐且依赖专家经验的校准。这种部署鸿沟——即机器人的“脊髓”——仍然是具身智能（Embodied AI）实现规模化的主要瓶颈。

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.

因此，我们提出了 SPINE（基于智能体专业知识的可扩展物理集成）：这是一个智能体框架，旨在让非机器人专业人员也能系统地调试和部署双臂机器人。SPINE 的核心包含两个协同工作的多智能体工作流：一个是用于创建机器人特定配置文件的“配置构建器”，另一个是循环执行诊断、修复和验证，直至实现远程操作的“调试器”。

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.

在七个 DOBOT X-Trainer 调试场景中，使用 SPINE 的机器人技术新手表现优于使用 Claude Code 且拥有相同参考资料但缺乏 SPINE 结构化工作流的人类操作员。其操作成功率从 75% 提升至 100%，平均远程操作准备时间从 16 分 45 秒缩短至 13 分 47 秒。

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.

在另一种基于 ROS/CAN 的双臂机器人 AgileX PiPER 上，SPINE 成功解决了所有 10 个预设故障，而专家基准测试仅解决了 10 个中的 9 个，且耗时几乎相同。综上所述，这些结果表明 SPINE 能够跨双臂平台迁移，减少对专家校准的依赖，并推动具身智能向可扩展的现实世界部署迈进。