Multilateral surgical pattern cutting in 2D orthotropic gauze with deep reinforcement learning policies for tensioning
Abstract
In the Fundamentals of Laparoscopic Surgery (FLS) standard medical training regimen, the Pattern Cutting task requires residents to demonstrate proficiency by maneuvering two tools, surgical scissors and tissue gripper, to accurately cut a circular pattern on surgical gauze suspended at the corners. Accuracy of cutting depends on tensioning, wherein the gripper pinches a point on the gauze in R3and pulls to induce and maintain tension in the material as cutting proceeds. An automated tensioning policy maps the current state of the gauze to output a direction of pulling as an action. The optimal tensioning policy depends on both the choice of pinch point and cutting trajectory. We explore the problem of learning a tensioning policy conditioned on specific cutting trajectories. Every timestep, we allow the gripper to react to the deformation of the gauze and progress of the cutting trajectory with a translation unit vector along an allowable set of directions. As deformation is difficult to analytically model and explicitly observe, we leverage deep reinforcement learning with direct policy search methods to learn tensioning policies using a finite-element simulator and then transfer them to a physical system. We compare the Deep RL tensioning policies with fixed and analytic (opposing the error vector with a fixed pinch point) policies on a set of 17 open and closed curved contours in simulation and 4 patterns in physical experiments with the da Vinci Research Kit (dVRK). Our simulation results suggest that learning to tension with Deep RL can significantly improve performance and robustness to noise and external forces.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Recently, deep reinforcement learning has been used for autonomous robot control, e.g., self-driving vehicle [203], [199], medical robot [213], mobile service robot.
“Recently, deep reinforcement learning has been used for autonomous robot control, e.g., self-driving vehicle [203], [199], medical robot [213], mobile service robot.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [213]
Deep reinforcement learning has been used to control the surgical robot for multilateral cutting in 2D orthotropic gauze.
“Recently, deep reinforcement learning has been used to control the surgical robot [213] for multilateral cutting in 2d orthotropic gauze.”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [213]
Cites
Trust region policy optimization
Schulman, J., Levine, S., Moritz, P., Jordan, M. I., & Abbeel, P. (2015) · arXiv · doi:10.48550/arxiv.1502.05477 · Cited by 4,578 · see citing works
End-to-End Training of Deep Visuomotor PoliciesPreprint
Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2015) · Journal of machine learning research · doi:10.48550/arxiv.1504.00702 · Cited by 4,011 · see citing works
A survey of robot learning from demonstration
Argall, B. D., Chernova, S., Veloso, M., & Browning, B. (2009) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2008.10.024 · Cited by 3,227 · see citing works
Reinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160 · see citing works
Laparoscopic surgery versus open surgery for colon cancer: short-term outcomes of a randomised trial
Veldkamp, R. (2005) · The Lancet Oncology · doi:10.1016/s1470-2045(05)70221-7 · Cited by 2,339 · see citing works
Deep learning for detecting robotic grasps
Lenz, I., Lee, H., & Saxena, A. (2013) · Int. J. Robotics Res. · doi:10.1177/0278364914549607 · Cited by 1,820 · see citing works
Benchmarking Deep Reinforcement Learning for Continuous Control
Duan, Y., Chen, X., Houthooft, R., Schulman, J., & Abbeel, P. (2016) · arXiv · doi:10.48550/arxiv.1604.06778 · Cited by 1,114 · see citing works
An open-source research kit for the da Vinci® Surgical System
Kazanzides, P., Chen, Z., Deguet, A., Fischer, G. S., Taylor, R. H., & DiMaio, S. (2014) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2014.6907809 · Cited by 509 · see citing works
Ritter, E. M., & Scott, D. J. (2007) · Surgical Innovation · doi:10.1177/1553350607302329 · Cited by 332 · see citing works
Evolution of autonomous and semi‐autonomous robotic surgical systems: a review of the literature
Moustris, G. P., Hiridis, S., Deliparaschos, K. M., & Konstantinidis, M. K. (2011) · International Journal of Medical Robotics and Computer Assisted Surgery · doi:10.1002/rcs.408 · Cited by 323 · see citing works
Medical Robots: Current Systems and Research Directions
Beasley, R. A. (2012) · Journal of Robotics · doi:10.1155/2012/401613 · Cited by 312 · see citing works
van den Berg, J., Miller, S., Duckworth, D., Hu, H., Wan, A., Fu, X., Goldberg, K., & Abbeel, P. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509621 · Cited by 233 · see citing works
Self-citationRobotic roommates making pancakes
Beetz, M., Klank, U., Kresse, I., Maldonado, A., Mösenlechner, L., Pangercic, D., Rühr, T., & Tenorth, M. (2011) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids.2011.6100855 · Cited by 220 · see citing works
Using inaccurate models in reinforcement learning
Abbeel, P., Quigley, M., & Ng, A. Y. (2006) · International Conference on Machine Learning · doi:10.1145/1143844.1143845 · Cited by 214 · see citing works
Stegemann, A. P., Ahmed, K., Syed, J. R., Rehman, S., Ghani, K. R., Autorino, R., Sharif, M. A., Rao, A. R., Shi, Y. Y., Wilding, G. E., et al. (2013) · Urology · doi:10.1016/j.urology.2012.12.033 · Cited by 185 · see citing works
Murali, A., Sen, S., Kehoe, B., Garg, A., McFarland, S., Patil, S., Boyd, W. D., Lim, S., Abbeel, P., & Goldberg, K. (2015) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2015.7139344 · Cited by 178 · see citing works
Self-citationHunt, K. H. (1973) · Mechanism and Machine Theory · doi:10.1016/0094-114x(73)90012-8 · Cited by 166 · see citing works
Interactive simulation of surgical needle insertion and steering
Chentanez, N., Alterovitz, R., Ritchie, D., Cho, L., Hauser, K. K., Goldberg, K., Shewchuk, J. R., & O’Brien, J. F. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531394 · Cited by 161 · see citing works
Self-citationSen, S., Garg, A., Gealy, D. V., McKinley, S., Jen, Y., & Goldberg, K. Y. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487611 · Cited by 159 · see citing works
Self-citationA Surgery Simulation Supporting Cuts and Finite Element Deformation
Nienhuys, H.-W., & van der Stappen, A. F. (2001) · Lecture notes in computer science · doi:10.1007/3-540-45468-3_18 · Cited by 131 · see citing works
Using linked volumes to model object collisions, deformation, cutting, carving, and joining
Frisken-Gibson, S. F. (1999) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/2945.817350 · Cited by 122 · see citing works
Motion generation of robotic surgical tasks: Learning from expert demonstrations
Reiley, C. E., Plaku, E., & Hager, G. D. (2010) · Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/iembs.2010.5627594 · Cited by 98 · see citing works
Autonomous drifting using simulation-aided reinforcement learning
Cutler, M., & How, J. P. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487756 · Cited by 88 · see citing works
Schulman, J., Gupta, A., Venkatesan, S., Tayson-Frederick, M., & Abbeel, P. (2013) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2013.6696945 · Cited by 84 · see citing works
Tumor localization using automated palpation with Gaussian Process Adaptive Sampling
Garg, A., Sen, S., Kapadia, R., Jen, Y., McKinley, S., Miller, L., & Goldberg, K. Y. (2016) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2016.7743380 · Cited by 72 · see citing works
Self-citationMedical Robotics and Computer-Integrated Surgery
Taylor, R. H., Menciassi, A., Gabor Fichtinger, A., & Dario, P. (2008) · Springer eBooks · doi:10.1007/978-3-540-30301-5_53 · Cited by 71 · see citing works
Modeling the Forces of Cutting With Scissors
Mahvash, M., Voo, L. M., Kim, D. J., Jeung, K. A., Wainer, J., & Okamura, A. M. (2008) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2007.908069 · Cited by 64 · see citing works
Real-World Reinforcement Learning via Multifidelity Simulators
Cutler, M., Walsh, T. J., & How, J. P. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2419431 · Cited by 61 · see citing works
Combining imitation and reinforcement learning to fold deformable planar objects
Balaguer, B., & Carpin, S. (2011) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2011.6094992 · Cited by 59 · see citing works
Online Trajectory Planning in Dynamic Environments for Surgical Task Automation
Osa, T., Sugita, N., & Mitsuishi, M. (2014) · Robotics: Science and Systems · doi:10.15607/rss.2014.x.011 · Cited by 48 · see citing works
Automation of Manual Tasks for Minimally Invasive Surgery
Mayer, H. G., Nagy, I., Burschka, D., Knoll, A., Braun, E. U., Lange, R., & Bauernschmitt, R. (2008) · International Conference on Networking and Services · doi:10.1109/icas.2008.16 · Cited by 37 · see citing works
On cutting and dissection of virtual deformable objects
Zhang, H., Payandeh, S., & Dill, J. C. (2004) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2004.1308877 · Cited by 32 · see citing works
Patterning of tensile fabric structures with a discrete element model using dynamic relaxation
Gale, S., & Lewis, W. J. (2016) · Computers & Structures · doi:10.1016/j.compstruc.2016.03.005 · Cited by 28 · see citing works
Motion Planning in Medicine: Optimization and Simulation Algorithms for Image-Guided Procedures
Alterovitz, R., & Goldberg, K. (2008) · Springer tracts in advanced robotics · doi:10.1007/978-3-540-69259-1 · Cited by 28 · see citing works
Self-citationPolicy Learning with Continuous Memory States for Partially Observed Robotic Control.
Zhang, M., Levine, S., McCarthy, Z., Finn, C., & Abbeel, P. (2015) · Cited by 11 · see citing works
Krishnan, S., Garg, A., Liaw, R., Thananjeyan, B., Miller, L., Pokorny, F. T., & Goldberg, K. Y. (2020) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-43089-4_43 · Cited by 7 · see citing works
Self-citation
Flagged first, then by citations
2 of the 38 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Deep Learning Applications in Medical Image Analysis
Ker, J., Wang, L., Rao, J. P., & Lim, C. C. T. (2017) · IEEE Access · doi:10.1109/access.2017.2788044 · Cited by 1,521 · see citing works
Zhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
Reinforcement Learning in Healthcare: A Survey
Yu, C., Liu, J., Nemati, S., & Yin, G. (2021) · ACM Computing Surveys · doi:10.1145/3477600 · Cited by 680 · see citing works
Reinforcement learning for intelligent healthcare applications: A survey
Coronato, A., Naeem, M., De Pietro, G., & Paragliola, G. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101964 · Cited by 320 · see citing works
Sim-to-real reinforcement learning for deformable object manipulation
Matas, J., James, S., & Davison, A. J. (2018) · arXiv · doi:10.48550/arxiv.1806.07851 · Cited by 241 · see citing works
Martín-Martín, R., Lee, M. A., Gardner, R., Savarese, S., Bohg, J., & Garg, A. (2019) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968201 · Cited by 186 · see citing works
Self-citationApplication of artificial intelligence in surgery
Zhou, X.-Y., Guo, Y., Shen, M., & Yang, G.-Z. (2020) · Frontiers of Medicine · doi:10.1007/s11684-020-0770-0 · Cited by 181 · see citing works
Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit
D’Ettorre, C., Mariani, A., Stilli, A., y Baena, F. R., Valdastri, P., Deguet, A., Kazanzides, P., Taylor, R. H., Fischer, G. S., DiMaio, S., et al. (2021) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2021.3101646 · Cited by 163 · see citing works
A Comprehensive Survey of the Tactile Internet: State-of-the-Art and Research Directions
Promwongsa, N., Ebrahimzadeh, A., Naboulsi, D., Kianpisheh, S., Belqasmi, F., Glitho, R., Crespi, N., & Alfandi, O. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.3025995 · Cited by 151 · see citing works
Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence
Gou, F., Liu, J., Xiao, C., & Wu, J. (2024) · Diagnostics · doi:10.3390/diagnostics14141472 · Cited by 128 · see citing works
Adversarially Robust Policy Learning: Active construction of physically-plausible perturbations
Mandlekar, A., Zhu, Y., Garg, A., Fei-Fei, L., & Savarese, S. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206245 · Cited by 116 · see citing works
Self-citationSRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learning
Kim, J. W., Chen, J.-T., Hansen, P., Shi, L. X., Goldenberg, A., Schmidgall, S., Scheikl, P. M., Deguet, A., White, B. M., Tsai, D. R., et al. (2025) · Science Robotics · doi:10.1126/scirobotics.adt5254 · Cited by 121 · see citing works
Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor
Seita, D., Ganapathi, A., Hoque, R., Hwang, M., Cen, E., Tanwani, A. K., Balakrishna, A., Thananjeyan, B., Ichnowski, J., Jamali, N., et al. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341608 · Cited by 113 · see citing works
Self-citationSuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation With Surgical Robotics
Li, Y., Richter, F., Lu, J., Funk, E. K., Orosco, R. K., Zhu, J., & Yip, M. C. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.2970659 · Cited by 111 · see citing works
Learning Rope Manipulation Policies Using Dense Object Descriptors Trained on Synthetic Depth Data
Sundaresan, P., Grannen, J., Thananjeyan, B., Balakrishna, A., Laskey, M., Stone, K., Gonzalez, J. E., & Goldberg, K. (2020) · 2020 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra40945.2020.9197121 · Cited by 107 · see citing works
Self-citationXu, J., Li, B., Lu, B., Liu, Y., Dou, Q., & Heng, P. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9635867 · Cited by 107 · see citing works
Concepts and Trends in Autonomy for Robot-Assisted Surgery
Fiorini, P., Goldberg, K. Y., Liu, Y., & Taylor, R. H. (2022) · Proceedings of the IEEE · doi:10.1109/jproc.2022.3176828 · Cited by 94 · see citing works
Self-citationDeep Transfer Learning of Pick Points on Fabric for Robot Bed-Making
Seita, D., Jamali, N., Laskey, M., Tanwani, A. K., Berenstein, R., Baskaran, P., Iba, S., Canny, J., & Goldberg, K. Y. (2022) · Springer proceedings in advanced robotics · doi:10.1007/978-3-030-95459-8_17 · Cited by 88 · see citing works
Self-citationSoft Tissue Simulation Environment to Learn Manipulation Tasks in Autonomous Robotic Surgery
Tagliabue, E., Pore, A., Dall’Alba, D., Magnabosco, E., Piccinelli, M., & Fiorini, P. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341710 · Cited by 79 · see citing works
Dynamics Randomization Revisited: A Case Study for Quadrupedal Locomotion
Xie, Z., Da, X., van de Panne, M., Babich, B., & Garg, A. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560837 · Cited by 78 · see citing works
Self-citationScheikl, P. M., Tagliabue, E., Gyenes, B., Wagner, M., Dall’Alba, D., Fiorini, P., & Mathis-Ullrich, F. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3227873 · Cited by 81 · see citing works
Long, Y., Wang, W., Huang, T., Wang, Y., & Dou, Q. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3284380 · Cited by 75 · see citing works
VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation
Hoque, R., Seita, D., Balakrishna, A., Ganapathi, A., Tanwani, A., Jamali, N., Yamane, K., Iba, S., & Goldberg, K. (2020) · Robotics: Science and Systems XVI · doi:10.15607/rss.2020.xvi.034 · Cited by 74 · see citing works
Self-citationLu, J., Jayakumari, A., Richter, F., Li, Y., & Yip, M. C. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561249 · Cited by 71 · see citing works
Open-Sourced Reinforcement Learning Environments for Surgical Robotics
Richter, F., Orosco, R. K., & Yip, M. C. (2019) · arXiv · doi:10.48550/arxiv.1903.02090
Seita, D., Krishnan, S., Fox, R., McKinley, S., Canny, J., & Goldberg, K. Y. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8460583 · Cited by 67 · see citing works
Self-citationDiSECt: A Differentiable Simulation Engine for Autonomous Robotic Cutting
Heiden, E., Macklin, M., Narang, Y., Fox, D., Garg, A., & Ramos, F. (2021) · Robotics: Science and Systems XVII · doi:10.15607/rss.2021.xvii.067 · Cited by 66 · see citing works
Self-citationAutomated Pick-Up of Suturing Needles for Robotic Surgical Assistance
D’Ettorre, C., Dwyer, G., Du, X., Chadebecq, F., Vasconcelos, F., Momi, E. D., & Stoyanov, D. V. (2018) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2018.8461200 · Cited by 64 · see citing works
Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics
Ganapathi, A., Sundaresan, P., Thananjeyan, B., Balakrishna, A., Seita, D., Grannen, J., Hwang, M., Hoque, R., Gonzalez, J. E., Jamali, N., et al. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561980 · Cited by 69 · see citing works
Self-citationFuture of Artificial Intelligence in Surgery: A Narrative Review
Amin, A., Cardoso, S. A., Suyambu, J., Saboor, H. A., Cardoso, R. P., Husnain, A., Isaac, N. V., Backing, H., Mehmood, D., Mehmood, M., et al. (2024) · Cureus · doi:10.7759/cureus.51631 · Cited by 64 · see citing works
Deep Learning Based Image Processing for Robot Assisted Surgery: A Systematic Literature Survey
Hussain, S. M., Brunetti, A., Lucarelli, G. T., Memeo, R., Bevilacqua, V., & Buongiorno, D. (2022) · IEEE Access · doi:10.1109/access.2022.3223704 · Cited by 56 · see citing works
Manipulating Soft Tissues by Deep Reinforcement Learning for Autonomous Robotic Surgery
Nguyen, N. D., Nguyen, T. T., Nahavandi, S., Bhatti, A., & Guest, G. D. (2019) · IEEE International Systems Conference (SysCon) · doi:10.1109/syscon.2019.8836924 · Cited by 55 · see citing works
Richter, F., Shen, S., Liu, F., Huang, J.-B., Funk, E. K., Orosco, R. K., & Yip, M. C. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3056057 · Cited by 55 · see citing works
Hwang, M., Thananjeyan, B., Paradis, S., Seita, D., Ichnowski, J., Fer, D., Low, T., & Goldberg, K. (2020) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2020.3010746 · Cited by 51 · see citing works
Self-citationA Review on Deep Learning in Minimally Invasive Surgery
Rivas-Blanco, I., Pérez-del-Pulgar, C. J., Garcia-Morales, I., & Muñoz, V. F. (2021) · IEEE Access · doi:10.1109/access.2021.3068852 · Cited by 50 · see citing works
Visuospatial foresight for physical sequential fabric manipulation
Hoque, R., Seita, D., Balakrishna, A., Ganapathi, A., Tanwani, A. K., Jamali, N., Yamane, K., Iba, S., & Goldberg, K. (2022) · Autonomous Robots · doi:10.1007/s10514-021-10001-0 · Cited by 45 · see citing works
Self-citationPore, A., Corsi, D., Marchesini, E., Dall’Alba, D., Casals, A., Farinelli, A., & Fiorini, P. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636175 · Cited by 46 · see citing works
Baek, D., Hwang, M., Kim, H., & Kwon, D. (2018) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/urai.2018.8441801 · Cited by 45 · see citing works
Liu, F., Li, Z., Han, Y., Lu, J., Richter, F., & Yip, M. C. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561177 · Cited by 44 · see citing works
Applying Depth-Sensing to Automated Surgical Manipulation with a da Vinci Robot
Hwang, M., Seita, D., Thananjeyan, B., Ichnowski, J., Paradis, S., Fer, D., Low, T., & Goldberg, K. Y. (2020) · International Symposium on Medical Robotics · doi:10.1109/ismr48331.2020.9312948 · Cited by 40 · see citing works
Self-citationOu, Y., & Tavakoli, M. (2023) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2023.3254860 · Cited by 39 · see citing works
Orbit-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity
Yu, Q., Moghani, M., Dharmarajan, K., Schorp, V., Panitch, W. C.-H., Liu, J., Hari, K., Huang, H., Mittal, M., Goldberg, K. Y., et al. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611637 · Cited by 40 · see citing works
Self-citationSuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation With Surgical Robotics
Li, Y., Richter, F., Lu, J., Funk, E. K., Orosco, R. K., Zhu, J., & Yip, M. C. (2020) · eScholarship (California Digital Library) · Cited by 36 · see citing works
A New Tensioning Method using Deep Reinforcement Learning for Surgical Pattern Cutting
Nguyen, T. T., Nguyen, N. D., Bello, F., & Nahavandi, S. (2019) · IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2019.8755235 · Cited by 36 · see citing works
Hwang, M., Ichnowski, J., Thananjeyan, B., Seita, D., Paradis, S., Fer, D., Low, T., & Goldberg, K. (2022) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2022.3171795 · Cited by 36 · see citing works
Self-citationAutoBag: Learning to Open Plastic Bags and Insert Objects
Chen, L. Y., Shi, B., Seita, D., Cheng, R., Kollar, T., Held, D., & Goldberg, K. Y. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10161402 · Cited by 37 · see citing works
Self-citationHuang, T., Chen, K., Li, B., Liu, Y., & Dou, Q. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160327 · Cited by 34 · see citing works
Learning Visual Shape Control of Novel 3D Deformable Objects from Partial-View Point Clouds
Thach, B., Cho, B. Y., Kuntz, A., & Hermans, T. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812215 · Cited by 36 · see citing works
Paradis, S., Hwang, M., Thananjeyan, B., Ichnowski, J., Seita, D., Fer, D., Low, T., Gonzalez, J. E., & Goldberg, K. Y. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561070 · Cited by 34 · see citing works
Self-citationRoboCraft: Learning to see, simulate, and shape elasto-plastic objects in 3D with graph networks
Shi, H., Xu, H., Huang, Z., Li, Y., & Wu, J. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231219020 · Cited by 33 · see citing works
Schwaner, K. L., Dall’Alba, D., Jensen, P. T., Fiorini, P., & Savarimuthu, T. R. (2021) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49439.2021.9551569 · Cited by 33 · see citing works
RoboCraft: Learning to See, Simulate, and Shape Elasto-Plastic Objects with Graph Networks
Shi, H., Xu, H., Huang, Z., Li, Y., & Wu, J. (2022) · Robotics: Science and Systems · doi:10.15607/rss.2022.xviii.008 · Cited by 33 · see citing works
A new tensioning method using deep reinforcement learning for surgical pattern cutting
Nguyen, T. T., Nguyen, N. D., Bello, F., & Nahavandi, S. (2024) · Figshare · Cited by 32 · see citing works
Learning to Localize, Grasp, and Hand Over Unmodified Surgical Needles
Wilcox, A., Kerr, J. H., Thananjeyan, B., Ichnowski, J., Hwang, M., Paradis, S., Fer, D., & Goldberg, K. Y. (2022) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra46639.2022.9812393 · Cited by 32 · see citing works
Self-citationUntangling Dense Knots by Learning Task-Relevant KeypointsPreprint
Grannen, J., Sundaresan, P., Thananjeyan, B., Ichnowski, J., Balakrishna, A., Hwang, M., Viswanath, V., Laskey, M., Gonzalez, J. E., & Goldberg, K. Y. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04999 · Cited by 30 · see citing works
Self-citationColan, J., Nakanishi, J., Aoyama, T., & Hasegawa, Y. (2021) · Robotics · doi:10.3390/robotics10010027 · Cited by 30 · see citing works
Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots
Schmidgall, S., Krieger, A., & Eshraghian, J. K. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610448 · Cited by 29 · see citing works
Boabang, F., Ebrahimzadeh, A., Glitho, R., Elbiaze, H., Maier, M., & Belqasmi, F. (2021) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2021.3106577 · Cited by 27 · see citing works
Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention
Mo, K., Xia, C., Wang, X., Deng, Y., Gao, X., & Liang, B. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3229573 · Cited by 26 · see citing works
Sim2Real Rope Cutting With a Surgical Robot Using Vision-Based Reinforcement Learning
Haiderbhai, M., Gondokaryono, R., Wu, A. Y., & Kahrs, L. A. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3410297 · Cited by 23 · see citing works
Gao, H., Xiao, X., Qiu, L., Meng, M. Q. ‐., King, N. K. K., & Ren, H. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9560747 · Cited by 23 · see citing works
Cooperative Assistance in Robotic Surgery through Multi-Agent Reinforcement Learning
Scheikl, P. M., Gyenes, B., Davitashvili, T., Younis, R., Schulze, A., Müller-Stich, B. P., Neumann, G. M., Wagner, M., & Mathis-Ullrich, F. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636193 · Cited by 22 · see citing works
Integration of Reinforcement Learning in a Virtual Robotic Surgical Simulation
Bourdillon, A. T., Garg, A., Wang, H., Woo, Y.-P. J., Pavone, M., & Boyd, J. H. (2022) · Surgical Innovation · doi:10.1177/15533506221095298 · Cited by 22 · see citing works
Self-citationShahkoo, A. A., & Abin, A. A. (2023) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2023.3237772 · Cited by 22 · see citing works
The importance of machine learning in autonomous actions for surgical decision making
Wagner, M., Bodenstedt, S., Daum, M. T. J., Schulze, A., Younis, R., Brandenburg, J. M., Kolbinger, F. R., Distler, M., Maier-Hein, L., Weitz, J., et al. (2022) · Artificial Intelligence Surgery · doi:10.20517/ais.2022.02 · Cited by 22 · see citing works
AMBF-RL: A real-time simulation based Reinforcement Learning toolkit for Medical Robotics
Varier, V. M., Rajamani, D. K., Tavakkolmoghaddam, F., Munawar, A., & Fischer, G. S. (2022) · International Symposium on Medical Robotics · doi:10.1109/ismr48347.2022.9807609 · Cited by 20 · see citing works
Matl, C., & Bajcsy, R. K. (2021) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros51168.2021.9636808 · Cited by 20 · see citing works
MOPS: A Modular and Open Platform for Surgical Robotics Research
Schwaner, K. L., Iturrate, I., Andersen, J., Dam, C. R., Jensen, P. T., & Savarimuthu, T. R. (2021) · International Symposium on Medical Robotics · doi:10.1109/ismr48346.2021.9661539 · Cited by 19 · see citing works
Learning Robust Bed Making using Deep Imitation Learning with DARTPreprint
Laskey, M., Powers, C., Joshi, R., Poursohi, A., & Goldberg, K. Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.02525 · Cited by 18 · see citing works
Self-citationLearning-Based Fabric Folding and Box Wrapping
Wang, X., Zhao, J., Jiang, X., & Liu, Y. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3158434 · Cited by 16 · see citing works
A Multiarm Robotic Platform for Scientific Exploration: Its Design, Digital Twins, and Validation
Marinho, M. M., Quiroz-Omaña, J. J., & Harada, K. (2024) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2023.3336472 · Cited by 15 · see citing works
Huang, J.-B., Liu, F., Richter, F., & Yip, M. C. (2021) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48506.2021.9561624 · Cited by 15 · see citing works
Artificial intelligence and pediatric surgery: where are we?
Miyake, Y., Retrosi, G., & Keijzer, R. (2024) · Pediatric Surgery International · doi:10.1007/s00383-024-05921-8 · Cited by 14 · see citing works
Study on the Control Method and Optimization Experiment of Prostate Soft Tissue Puncture
Zhang, Y., Li, B., & Yuan, L. (2020) · IEEE Access · doi:10.1109/access.2020.3041370 · Cited by 14 · see citing works
Optimizing Robot-Assisted Surgery Suture Plans to Avoid Joint Limits and Singularities
Thananjeyan, B., Tanwani, A. K., Ji, J., Fer, D., Patel, V. V., Krishnan, S., & Goldberg, K. Y. (2019) · International Symposium on Medical Robotics · doi:10.1109/ismr.2019.8710194 · Cited by 12 · see citing works
Self-citationDeformNet: Latent Space Modeling and Dynamics Prediction for Deformable Object Manipulation
Li, C., Ai, Z., Wu, T., Li, X., Ding, W., & Xu, H. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611243 · Cited by 11 · see citing works
Deep reinforcement learning in continuous action space for autonomous robotic surgery
Shahkoo, A. A., & Abin, A. A. (2022) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-022-02789-8 · Cited by 11 · see citing works
Microinstrument contact force sensing based on cable tension using BLSTM–MLP network
Yu, L., Yu, X., & Zhang, Y. (2019) · Intelligent Service Robotics · doi:10.1007/s11370-019-00306-6 · Cited by 11 · see citing works
Reward Learning from Suboptimal Demonstrations with Applications in Surgical Electrocautery
Karimi, Z., Ho, S.-H., Thach, B., Kuntz, A., & Brown, D. S. (2024) · International Symposium on Medical Robotics · doi:10.1109/ismr63436.2024.10585785 · Cited by 11 · see citing works
An Ultrasound-Guided System for Autonomous Marking of Tumor Boundaries During Robot-Assisted Surgery
Marahrens, N., Jones, D. A., Murasovs, N., Biyani, C. S., & Valdastri, P. (2024) · IEEE Transactions on Medical Robotics and Bionics · doi:10.1109/tmrb.2024.3468397 · Cited by 10 · see citing works
Vision-based hand–eye calibration for robot-assisted minimally invasive surgery
Sun, Y., Pan, B., Guo, Y., Fu, Y., & Niu, G. (2020) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-020-02245-5 · Cited by 10 · see citing works
The Raven Open Surgical Robotic Platforms: A Review and Prospect
(2019) · Acta Polytechnica Hungarica · doi:10.12700/aph.16.8.2019.8.2 · Cited by 10 · see citing works
GESRsim: Gastrointestinal Endoscopic Surgical Robot Simulator
Gao, H., Zhang, Z., Li, C., Xiao, X., Qiu, L., Yang, X., Hao, R., Zuo, X., Li, Y., & Ren, H. (2022) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros47612.2022.9982138 · Cited by 11 · see citing works
Patel, V. V., Krishnan, S., Goncalves, A., Chen, C. G., Boyd, W. D., & Goldberg, K. Y. (2018) · International Symposium on Medical Robotics · doi:10.1109/ismr.2018.8333301 · Cited by 10 · see citing works
Self-citationA Survey of Behavior Learning Applications in Robotics -- State of the Art and PerspectivesPreprint
Fabisch, A., Petzoldt, C., Otto, M., & Kirchner, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.01868 · Cited by 15 · see citing works
DiSECt: a differentiable simulator for parameter inference and control in robotic cutting
Heiden, E., Macklin, M., Narang, Y., Fox, D., Garg, A., & Ramos, F. T. (2023) · Autonomous Robots · doi:10.1007/s10514-023-10094-9 · Cited by 9 · see citing works
Self-citationLive Image-Based Neurosurgical Guidance and Roadmap Generation Using Unsupervised Embedding
Sarwin, G., Carretta, A., Staartjes, V. E., Zoli, M., Mazzatenta, D., Regli, L., Serra, C., & Konukoğlu, E. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-34048-2_9 · Cited by 9 · see citing works
Deformable Object Manipulation With Constraints Using Path Set Planning and Tracking
Huang, J., Chu, X., Ma, X., & Au, K. W. S. (2023) · IEEE Transactions on Robotics · doi:10.1109/tro.2023.3306618 · Cited by 9 · see citing works
Dueling deep Q-networks for social awareness-aided spectrum sharing
Wang, Y., Li, X.-Y., Wan, P., Chang, L., & Deng, X. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00382-1 · Cited by 9 · see citing works
Kawaharazuka, K., Okada, K., & Inaba, M. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610059 · Cited by 8 · see citing works
Vision-Based Framework of Single Master Dual Slave Semi-Autonomous Surgical Robot System
Sun, Y., Pan, B., Qu, J., & Fu, Y. (2020) · IRBM · doi:10.1016/j.irbm.2020.06.005 · Cited by 8 · see citing works
Toward Gesture Recognition in Robot-Assisted Surgical Procedures
Huynhnguyen, H., & Buy, U. A. (2021) · International Conference on Societal Automation (SA) · doi:10.1109/sa51175.2021.9507175 · Cited by 8 · see citing works
Automating Vascular Shunt Insertion with the dVRK Surgical Robot
Dharmarajan, K., Panitch, W., Jiang, M., Srinivas, K., Shi, B., Avigal, Y., Huang, H., Low, T., Fer, D., & Goldberg, K. Y. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160966 · Cited by 8 · see citing works
Self-citationSuperhuman Surgical Peg Transfer Using Depth-Sensing and Deep Recurrent Neural Networks.Preprint
Hwang, M., Thananjeyan, B., Seita, D., Ichnowski, J., Paradis, S., Fer, D., Low, T., & Goldberg, K. Y. (2020) · arXiv (Cornell University) · Cited by 8 · see citing works
Self-citationModel-Based Off-Policy Deep Reinforcement Learning With Model-Embedding
Tan, X., Qu, C., Xiong, J., Zhang, J. Y., Qiu, X., & Jin, Y. (2024) · IEEE Transactions on Emerging Topics in Computational Intelligence · doi:10.1109/tetci.2024.3369636 · Cited by 7 · see citing works
Guo, N., Tian, J., Wang, L., Sun, K., Mi, L., Ming, H., Zhao, Z., & Sun, F. (2022) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2022.1016598 · Cited by 7 · see citing works
Dexterous Robotic Cutting Based on Fracture Mechanics and Force Control
Mu, X., Xue, Y., & Jia, Y.-B. (2023) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2023.3309784 · Cited by 7 · see citing works
Efficient data-driven joint-level calibration of cable-driven surgical robots
Peng, H., Lewis, A., Su, Y.-H., Shan, L., Chiang, D., Jiang, W., Lai, H., & Hannaford, B. (2024) · npj Robotics · doi:10.1038/s44182-024-00016-x · Cited by 6 · see citing works
Shi, Y., Li, J., Yang, Y., Li, Z., Fukuda, T., & Shi, C. (2025) · Advanced Robotics Research · doi:10.1002/adrr.202500074 · Cited by 6 · see citing works
Ondras, J., Ni, D., Deng, X., Gu, Z., Zheng, H., & Bhattacharjee, T. (2022) · International Conference on Control, Automation and Systems (ICCAS) · doi:10.23919/iccas55662.2022.10003767 · Cited by 6 · see citing works
- Authors
- Thananjeyan, B., Garg, A., Krishnan, S., Chen, C., Miller, L., & Goldberg, K.
- Affiliations
- University of California, Berkeley (US)
- Venue
- 2017 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 2371-2378
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2736750746
- Cited by
- 144
- References
- 38
- Citations by year
- 2026: 5 · 2025: 13 · 2024: 22 · 2023: 20 · 2022: 18 · 2021: 25 · 2020: 21 · 2019: 10 · 2018: 7 · 2017: 3
- FWCI
- 142.47
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- Topics
- Health Sciences › Medicine › Surgery › Surgical Simulation and Training
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Biomedical Engineering › Soft Robotics and Applications
- Keywords
- pattern cutting, deep reinforcement learning, surgical training, Fundamentals of Laparoscopic Surgery, surgical scissors, finite element analysis, da Vinci Research Kit, policy transfer, cutting trajectory, surgical skill assessment
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
