Abstract
Surgical robots have played an important role in guiding surgery toward precise interventions and targeted therapy. Aim to provide standard surgical solutions, autonomous surgical robotic systems can potentially improve the reproducibility and consistency of robot-assisted surgery. To meet the demands of enhancing the autonomy of surgical robots, we propose a novel model-independent training methodology for retraction and manipulation of deformable tissue in robotic surgery. We use contrastive optimization to learn deformable tissue’s underlying visual latent representations. Then we apply an improved version of Deep Deterministic Policy Gradients (DDPG) with asymmetric inputs to train an agent in a simulation environment. To evaluate the effectiveness and validity of our method, we tested our approach based on three different criteria. Compared to the state-of-the-art method, our method could especially accomplish safe tissue retraction task in constrained situations without collecting expert demonstrations.
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References
Wagner, M., Bodenstedt, S., Daum, M., et al.: The importance of machine learning in autonomous actions for surgical decision making. Art. Int. Surg. 2(2), 64–79 (2022). https://doi.org/10.20517/ais.2022.02
Patil, S., Alterovitz, R.: Toward automated tissue retraction in robot-assisted surgery. In: ICRA, IEEE, Anchorage, AK, USA, pp 2088–2094 (2010). https://doi.org/10.1109/ROBOT.2010.5509607
Attanasio, A., Scaglioni, B., Leonetti, M., et al.: Autonomous tissue retraction in robotic assisted minimally invasive surgery—A feasibility study. IEEE Robot. Autom. Lett. 5(4), 6528–6535 (2020). https://doi.org/10.1109/LRA.2020.3013914
Tagliabue, E., Dall’Alba, D., Pfeiffer, M., et al.: Data-driven intra-operative estimation of anatomical attachments for autonomous tissue dissection. IEEE Robot. Autom. Lett. 6(2), 1856-1863 (2021). https://doi.org/10.1109/LRA.2021.3060655
Retana, M., Nalamwar, K., Conyers, D.T., et al.: Autonomous data-driven manipulation of an unknown deformable tissue within constrained environments: a pilot study. In: ISMR IEEE, Georgia, USA, pp. 1-7(2022). https://doi.org/10.1109/ISMR48347.2022.9807519
Lillicrap, T.P., Hunt, J.J., Pritzel, A., et al.: Continuous control with deep reinforcement learning. In: ICLR, San Diego, CA, USA (2015). http://arxiv.org/abs/1509.02971
Kazanzides, P., Chen, Z., Deguet, A., et al.: An open-source research kit for the da Vinci® surgical system In: ICRA, IEEE, Hong Kong, pp 6434–6439 (2014). https://doi.org/10.1109/ICRA.2014.6907809
Tagliabue, E., Pore, A., Dall’Alba, D., et al.: Soft tissue simulation environment to learn manipulation tasks in autonomous robotic surgery. In: IROS, IEEE, Las Vegas, NV, USA, pp. 3261–3266 (2020). https://doi.org/10.1109/IROS45743.2020.9341710
Pore, A., Corsi, D., Marchesini, E., et al.: Safe reinforcement learning using formal verification for tissue retraction in autonomous robotic-assisted surgery. In: IROS, IEEE, Prague, Czech Republic, pp. 4025–4031(2021). https://doi.org/10.1109/IROS51168.2021.9636175
NVIDIA gameworks. Nvidia FleX. https://developer.nvidia.com/flex
Oord, A., Li, Y., Vinyals, O.: Representation learning with contrastive predictive coding. ar** for robot manipulation with environment uncertainty. ar**v e-prints (2020). http://arxiv.org/abs/2003.02740
Srinivas, A., Laskin, M., Abbeel, P.: CURL: contrastive unsupervised representations for reinforcement learning. ar**v e-prints (2020). http://arxiv.org/abs/2004.04136
Acknowledgment
The authors acknowledge supports from National Key Research and Development Program of China (2022YFC2405200), National Natural Science Foundation of China (82027807, U22A2051), Bei**g Municipal Natural Science Foundation (7212202), Tsinghua University Spring Breeze Fund (2021Z99CFY023), Institute for Intelligent Healthcare, Tsinghua University (2022ZLB001), and Tsinghua-Foshan Innovation Special Fund (2021THFS0104).
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Chen, J., Ma, L., Zhang, X., Liao, H. (2024). A Novel Model-Independent Approach for Autonomous Retraction of Soft Tissue. In: Wang, G., Yao, D., Gu, Z., Peng, Y., Tong, S., Liu, C. (eds) 12th Asian-Pacific Conference on Medical and Biological Engineering. APCMBE 2023. IFMBE Proceedings, vol 104. Springer, Cham. https://doi.org/10.1007/978-3-031-51485-2_10
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DOI: https://doi.org/10.1007/978-3-031-51485-2_10
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