From Instrument-Mounted Demonstrations to In-Vivo Execution: Learning Bimanual Laparoscopic Appendectomy Without Robot-Collected Demonstrations
From Instrument-Mounted Demonstrations to In-Vivo Execution: Learning Bimanual Laparoscopic Appendectomy Without Robot-Collected Demonstrations
This research presents an end-to-end pipeline for training a surgical robot policy using motion data captured from standard laparoscopic instruments. The system uses inertial, time-of-flight, and Hall sensors to track instrument state without external cameras. A diffusion policy is trained on both ex-vivo and in-vivo demonstrations, achieving appendectomy in three of four live rabbits. The robot serves as a timing reference and executor, with no demonstration collection.
This paper demonstrates the feasibility of training a bimanual surgical robot policy using instrument-mounted demonstrations without external cameras or trackers, validated on live animals. The approach leverages inertial and time-of-flight sensors for motion capture and uses a diffusion policy trained on both ex-vivo and in-vivo data.
Source: arXiv Robotics — research abstracts · Read original article ↗
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live rabbits
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tions we train a diffusion policy with a fine-tuned DINOv3 backbone, selecting its design by closed-loop rollouts in a physics simulator reconstructed from depth maps of an ex-vivo rabbit appendix. The policy is then retrained on 849 in-vivo demonstrations from four live rabbits and deployed on four additional live rabbits with electrosurgery armed. With the surgeon selecting the surgical phase, the policy completed
Open source S4additional live rabbits
4
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tions we train a diffusion policy with a fine-tuned DINOv3 backbone, selecting its design by closed-loop rollouts in a physics simulator reconstructed from depth maps of an ex-vivo rabbit appendix. The policy is then retrained on 849 in-vivo demonstrations from four live rabbits and deployed on four additional live rabbits with electrosurgery armed. With the surgeon selecting the surgical phase, the policy completed
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tions we train a diffusion policy with a fine-tuned DINOv3 backbone, selecting its design by closed-loop rollouts in a physics simulator reconstructed from depth maps of an ex-vivo rabbit appendix. The policy is then retrained on 849 in-vivo demonstrations from four live rabbits and deployed on four additional live rabbits with electrosurgery armed. With the surgeon selecting the surgical phase, the policy completed
Open source S4
ntroduce a surgical instrument-state logger that mounts on the shaft of a standard laparoscopic instrument and recovers its pose and jaw state from an inertial sensor, a time-of-flight sensor and a Hall sensor, with no external camera or tracker. A data pipeline measures the latency of every sensor channel against a robot ground truth and aligns the channels before forming observation-action pairs. On these demonstra
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Source：arXiv Robotics — research abstracts · arxiv.org