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arXiv · 2511.03882

Investigating Robot Control Policy Learning for Autonomous X-ray-guided Spine Procedures

Florence Klitzner·Blanca Inigo·Benjamin D. Killeen·Lalithkumar Seenivasan·Michelle Song·Axel Krieger·Mathias Unberath
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Abstract

Imitation learning-based robot control policies are enjoying renewed interest in video-based robotics. However, it remains unclear whether this approach applies to X-ray-guided procedures, such as spine instrumentation, with sparse inputs. We examine the feasibility, opportunities and challenges for imitation policy learning in bi-plane-guided cannula insertion. We develop an in silico sandbox for scalable, automated simulation of X-ray-guided spine procedures with a high degree of realism. We curate a dataset of correct trajectories and corresponding bi-planar X-ray sequences that emulate the stepwise alignment of providers. We then train imitation learning policies for planning and open-loop control that iteratively align a cannula in a vertebroplasty setting solely based on visual information. This precisely controlled setup offers insights into limitations and capabilities of this method. Our policy succeeded on the first attempt in 68.5% of cases, maintaining safe intra-pedicular trajectories across diverse vertebral levels. The policy transferred to complex anatomy, including fractures, as well as varied anatomies and initializations. Rollouts on real X-ray indicate that partial sim-to-real transfer with plausible trajectories is possible. While these preliminary results are promising, we also identify limitations, especially in entry point precision. The current results present a clear benchmark for future efforts, while with more robust priors and domain knowledge, such models may provide a foundation for future efforts toward lightweight and CT-free robotic intra-operative spinal navigation.

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Florence Klitzner, Blanca Inigo, Benjamin D. Killeen, Lalithkumar Seenivasan, Michelle Song, Axel Krieger, Mathias Unberath. 2026-05-22. Investigating Robot Control Policy Learning for Autonomous X-ray-guided Spine Procedures. https://doi.org/10.1007/s11548-026-03716-x

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