SPINE: Bridging the Cyber-Physical Gap with Agentic AI

2026-06-29

arXiv.org

AI Summary

为解决机器人高阶推理转化为可靠物理行为的难题，提出SPINE框架，通过子代理驱动的工作流程系统性调试和部署双臂机器人，提高成功率并减少操作时间。

Abstract

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.

Problem

• cyber-physical gap
• robotic integration
• embodied AI
• teleoperation

Innovation

• agentic AI
• cyber-physical integration
• bimanual robots
• teleoperation
• debugging