Ji Woong Kim

All Publications

• ImitateCholec: A Multimodal Dataset for Long-Horizon Imitation Learning in Robotic Cholecystectomy. Scientific data Hansen, P., Kim, J. W., Goldenberg, A., Chen, J. T., Li, Y. A., Deguet, A., White, B., Tsai, D. R., Cha, R., Jopling, J., Scheikl, P. M., Krieger, A. 2026

  Abstract

  The growing global shortage of skilled surgeons underscores the need for intelligent, assistive technologies in the operating room. To address this challenge, we introduce ImitateCholec, a publicly available dataset specifically designed to advance autonomous robotic systems during the critical clipping and cutting phase of laparoscopic cholecystectomy. The dataset comprises over 18,000 demonstrations from 34 ex vivo porcine cholecystectomies, totaling approximately 20 hours of data. Each clipping and cutting phase recorded in the dataset is segmented into 17 distinct surgical tasks. ImitateCholec uniquely integrates endoscopic videos captured from multiple camera perspectives with comprehensive kinematic data acquired through the da Vinci Research Kit. Both optimal demonstration executions and recovery maneuvers were systematically recorded, enabling the training of imitation learning models capable of robustly addressing real-world surgical variability. Primarily, ImitateCholec facilitates imitation learning for long-horizon surgical workflow execution, significantly advancing the development of autonomous robotic systems toward achieving phase-level autonomy and, ultimately, full procedural autonomy. Additional supported applications include surgical workflow modeling, error recognition, and surgical tool pose estimation.

  View details for DOI 10.1038/s41597-025-06526-z

  View details for PubMedID 41540078

• Will your next surgeon be a robot? Autonomy and AI in robotic surgery SCIENCE ROBOTICS Schmidgall, S., Opfermann, J. D., Kim, J., Krieger, A. 2025; 10 (104): eadt0187

  Abstract

  State-of-the-art surgery is performed robotically under direct surgeon control. However, surgical outcome is limited by the availability, skill, and day-to-day performance of the operating surgeon. What will it take to improve surgical outcomes independent of human limitations? In this Review, we explore the technological evolution of robotic surgery and current trends in robotics and artificial intelligence that could lead to a future generation of autonomous surgical robots that will outperform today's teleoperated robots.

  View details for DOI 10.1126/scirobotics.adt0187

  View details for Web of Science ID 001533518500002

  View details for PubMedID 40700524

• SRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learning. Science robotics 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., Cha, R. J., Jopling, J., Finn, C., Krieger, A. 2025; 10 (104): eadt5254

  Abstract

  Research on autonomous surgery has largely focused on simple task automation in controlled environments. However, real-world surgical applications demand dexterous manipulation over extended durations and robust generalization to the inherent variability of human tissue. These challenges remain difficult to address using existing logic-based or conventional end-to-end learning strategies. To address this gap, we propose a hierarchical framework for performing dexterous, long-horizon surgical steps. Our approach uses a high-level policy for task planning and a low-level policy for generating low-level trajectories. The high-level planner plans in language space, generating task-level or corrective instructions that guide the robot through the long-horizon steps and help recover from errors made by the low-level policy. We validated our framework through ex vivo experiments on cholecystectomy, a commonly practiced minimally invasive procedure, and conducted ablation studies to evaluate key components of the system. Our method achieves a 100% success rate across eight different ex vivo gallbladders, operating fully autonomously without human intervention. The hierarchical approach improved the policy's ability to recover from suboptimal states that are inevitable in the highly dynamic environment of realistic surgical applications. This work demonstrates step-level autonomy in a surgical procedure, marking a milestone toward clinical deployment of autonomous surgical systems.

  View details for DOI 10.1126/scirobotics.adt5254

  View details for PubMedID 40632876

• Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks Kim, J., Zhao, T. Z., Schmidgall, S., Deguet, A., Kobilarov, M., Finn, C., Krieger, A. edited by Kroemer, O., Agrawal, P., Burgard, W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024

  View details for Web of Science ID 001483833800007