SPINE: Bridging the Cyber-Physical Gap with Agentic AI
For researchers and practitioners in embodied AI, SPINE offers a scalable solution to the robot deployment bottleneck, reducing the need for expert calibration and accelerating real-world deployment.
SPINE is an agentic framework that automates the debugging and deployment of bimanual robots, reducing reliance on expert calibration. A novice using SPINE achieved 100% operationalization success (vs. 75% without) and reduced time-to-teleoperation from 16 min 45 s to 13 min 47 s on a DOBOT X-Trainer, and resolved all 10 implanted bugs on an AgileX PiPER (vs. 9/10 for an expert).
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.