Showing 1–7 of 7 results for author: Abdelaal, A E

Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics

Authors: Open-H-Embodiment Consortium, :, Nigel Nelson, Juo-Tung Chen, Jesse Haworth, Xinhao Chen, Lukas Zbinden, Dianye Huang, Alaa Eldin Abdelaal, Alberto Arezzo, Ayberk Acar, Farshid Alambeigi, Carlo Alberto Ammirati, Yunke Ao, Pablo David Aranda Rodriguez, Soofiyan Atar, Mattia Ballo, Noah Barnes, Federica Barontini, Filip Binkiewicz, Peter Black, Sebastian Bodenstedt, Leonardo Borgioli, Nikola Budjak, Benjamin Calmé , et al. (191 additional authors not shown)

Abstract: Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medical robotics has been limited by a fundamental data problem: existing medical robotic datasets are small, single-embodiment, and rarely shared openly, restricting the development of foundation models that the field needs… ▽ More Autonomous medical robots hold promise to improve patient outcomes, reduce provider workload, democratize access to care, and enable superhuman precision. However, autonomous medical robotics has been limited by a fundamental data problem: existing medical robotic datasets are small, single-embodiment, and rarely shared openly, restricting the development of foundation models that the field needs to advance. We introduce Open-H-Embodiment, the largest open dataset of medical robotic video with synchronized kinematics to date, spanning more than 50 institutions and multiple robotic platforms including the CMR Versius, Intuitive Surgical's da Vinci, da Vinci Research Kit (dVRK), Rob Surgical BiTrack, Virtual Incision's MIRA, Moon Surgical Maestro, and a variety of custom systems, spanning surgical manipulation, robotic ultrasound, and endoscopy procedures. We demonstrate the research enabled by this dataset through two foundation models. GR00T-H is the first open foundation vision-language-action model for medical robotics, which is the only evaluated model to achieve full end-to-end task completion on a structured suturing benchmark (25% of trials vs. 0% for all others) and achieves 64% average success across a 29-step ex vivo suturing sequence. We also train Cosmos-H-Surgical-Simulator, the first action-conditioned world model to enable multi-embodiment surgical simulation from a single checkpoint, spanning nine robotic platforms and supporting in silico policy evaluation and synthetic data generation for the medical domain. These results suggest that open, large-scale medical robot data collection can serve as critical infrastructure for the research community, enabling advances in robot learning, world modeling, and beyond. △ Less

Submitted 4 June, 2026; v1 submitted 22 April, 2026; originally announced April 2026.

Comments: Project website: https://open-h.github.io/open-h-embodiment/

Model selection and real-time skill assessment for suturing in robotic surgery

Authors: Zhaoyang Jacopo Hu, Alex Ranne, Alaa Eldin Abdelaal, Kiran Bhattacharyya, Etienne Burdet, Allison M. Okamura, Ferdinando Rodriguez y Baena

Abstract: Automated feedback systems have the potential to provide objective skill assessment for training and evaluation in robot-assisted surgery. In this study, we examine methods to achieve real-time prediction of surgical skill level in real-time based on Objective Structured Assessment of Technical Skills (OSATS) scores. Using data acquired from the da Vinci Surgical System, we carry out three main an… ▽ More Automated feedback systems have the potential to provide objective skill assessment for training and evaluation in robot-assisted surgery. In this study, we examine methods to achieve real-time prediction of surgical skill level in real-time based on Objective Structured Assessment of Technical Skills (OSATS) scores. Using data acquired from the da Vinci Surgical System, we carry out three main analyses, focusing on model design, their real-time performance, and their skill-level-based cross-validation training. For the model design, we evaluate the effectiveness of multimodal deep learning models for predicting surgical skill levels using synchronized kinematic and vision data. Our models include separate unimodal baselines and fusion architectures that integrate features from both modalities and are evaluated using mean Spearman's correlation coefficients, demonstrating that the fusion model consistently outperforms unimodal models for real-time predictions. For the real-time performance, we observe the prediction's trend over time and highlight correlation with the surgeon's gestures. For the skill-level-based cross-validation, we separately trained models on surgeons with different skill levels, which showed that high-skill demonstrations allow for better performance than those trained on low-skilled ones and generalize well to similarly skilled participants. Our findings show that multimodal learning allows more stable fine-grained evaluation of surgical performance and highlights the value of expert-level training data for model generalization. △ Less

Submitted 17 January, 2026; originally announced January 2026.

AutoCam: Hierarchical Path Planning for an Autonomous Auxiliary Camera in Surgical Robotics

Authors: Alexandre Banks, Randy Moore, Sayem Nazmuz Zaman, Alaa Eldin Abdelaal, Septimiu E. Salcudean

Abstract: Incorporating an autonomous auxiliary camera into robot-assisted minimally invasive surgery (RAMIS) enhances spatial awareness and eliminates manual viewpoint control. Existing path planning methods for auxiliary cameras track two-dimensional surgical features but do not simultaneously account for camera orientation, workspace constraints, and robot joint limits. This study presents AutoCam: an au… ▽ More Incorporating an autonomous auxiliary camera into robot-assisted minimally invasive surgery (RAMIS) enhances spatial awareness and eliminates manual viewpoint control. Existing path planning methods for auxiliary cameras track two-dimensional surgical features but do not simultaneously account for camera orientation, workspace constraints, and robot joint limits. This study presents AutoCam: an automatic auxiliary camera placement method to improve visualization in RAMIS. Implemented on the da Vinci Research Kit, the system uses a priority-based, workspace-constrained control algorithm that combines heuristic geometric placement with nonlinear optimization to ensure robust camera tracking. A user study (N=6) demonstrated that the system maintained 99.84% visibility of a salient feature and achieved a pose error of 4.36 $\pm$ 2.11 degrees and 1.95 $\pm$ 5.66 mm. The controller was computationally efficient, with a loop time of 6.8 $\pm$ 12.8 ms. An additional pilot study (N=6), where novices completed a Fundamentals of Laparoscopic Surgery training task, suggests that users can teleoperate just as effectively from AutoCam's viewpoint as from the endoscope's while still benefiting from AutoCam's improved visual coverage of the scene. These results indicate that an auxiliary camera can be autonomously controlled using the da Vinci patient-side manipulators to track a salient feature, laying the groundwork for new multi-camera visualization methods in RAMIS. △ Less

Submitted 15 May, 2025; originally announced May 2025.

Comments: 13 pages, 9 figures

ACM Class: J.3.2; J.2.7; I.2.9

Force-Aware Autonomous Robotic Surgery

Authors: Alaa Eldin Abdelaal, Jiaying Fang, Tim N. Reinhart, Jacob A. Mejia, Tony Z. Zhao, Jeannette Bohg, Allison M. Okamura

Abstract: This work demonstrates the benefits of using tool-tissue interaction forces in the design of autonomous systems in robot-assisted surgery (RAS). Autonomous systems in surgery must manipulate tissues of different stiffness levels and hence should apply different levels of forces accordingly. We hypothesize that this ability is enabled by using force measurements as input to policies learned from hu… ▽ More This work demonstrates the benefits of using tool-tissue interaction forces in the design of autonomous systems in robot-assisted surgery (RAS). Autonomous systems in surgery must manipulate tissues of different stiffness levels and hence should apply different levels of forces accordingly. We hypothesize that this ability is enabled by using force measurements as input to policies learned from human demonstrations. To test this hypothesis, we use Action-Chunking Transformers (ACT) to train two policies through imitation learning for automated tissue retraction with the da Vinci Research Kit (dVRK). To quantify the effects of using tool-tissue interaction force data, we trained a "no force policy" that uses the vision and robot kinematic data, and compared it to a "force policy" that uses force, vision and robot kinematic data. When tested on a previously seen tissue sample, the force policy is 3 times more successful in autonomously performing the task compared with the no force policy. In addition, the force policy is more gentle with the tissue compared with the no force policy, exerting on average 62% less force on the tissue. When tested on a previously unseen tissue sample, the force policy is 3.5 times more successful in autonomously performing the task, exerting an order of magnitude less forces on the tissue, compared with the no force policy. These results open the door to design force-aware autonomous systems that can meet the surgical guidelines for tissue handling, especially using the newly released RAS systems with force feedback capabilities such as the da Vinci 5. △ Less

Submitted 20 January, 2025; originally announced January 2025.

Gaze-based Learning from Demonstration In Surgical Robotics

Authors: A. E. Abdelaal, S. N. Zaman, P. Y Chen, T. Suzuki, J. Ingleton

Abstract: Surgical robotics is a rising field in medical technology and advanced robotics. Robot assisted surgery, or robotic surgery, allows surgeons to perform complicated surgical tasks with more precision, automation, and flexibility than is possible for traditional surgical approaches. The main type of robot assisted surgery is minimally invasive surgery, which could be automated and result in a faster… ▽ More Surgical robotics is a rising field in medical technology and advanced robotics. Robot assisted surgery, or robotic surgery, allows surgeons to perform complicated surgical tasks with more precision, automation, and flexibility than is possible for traditional surgical approaches. The main type of robot assisted surgery is minimally invasive surgery, which could be automated and result in a faster healing time for the patient. The surgical robot we are particularly interested in is the da Vinci surgical system, which is developed and manufactured by Intuitive Surgical. In the current iteration of the system, the endoscopic camera arm on the da Vinci robot has to be manually controlled and calibrated by the surgeon during a surgical task, which interrupts the flow of the operation. The main goal of this capstone project is to automate the motion of the camera arm using a probabilistic model based on surgeon eye gaze data and da Vinci robot kinematic data. △ Less

Submitted 1 November, 2023; originally announced November 2023.

Comments: 45 pages, Lots of Figures

ACM Class: F.2.2; I.2.7

The Quiet Eye Phenomenon in Minimally Invasive Surgery

Authors: Alaa Eldin Abdelaal, Rachelle Van Rumpt, Sayem Nazmuz Zaman, Irene Tong, Anthony Jarc, Gary L. Gallia, Masaru Ishii, Gregory D. Hager, Septimiu E. Salcudean

Abstract: In this paper, we report our discovery of a gaze behavior called Quiet Eye (QE) in minimally invasive surgery. The QE behavior has been extensively studied in sports training and has been associated with higher level of expertise in multiple sports. We investigated the QE behavior in two independently collected data sets of surgeons performing tasks in a sinus surgery setting and a robotic surgery… ▽ More In this paper, we report our discovery of a gaze behavior called Quiet Eye (QE) in minimally invasive surgery. The QE behavior has been extensively studied in sports training and has been associated with higher level of expertise in multiple sports. We investigated the QE behavior in two independently collected data sets of surgeons performing tasks in a sinus surgery setting and a robotic surgery setting, respectively. Our results show that the QE behavior is more likely to occur in successful task executions and in performances of surgeons of high level of expertise. These results open the door to use the QE behavior in both training and skill assessment in minimally invasive surgery. △ Less

Submitted 6 September, 2023; originally announced September 2023.

Orientation Matters: 6-DoF Autonomous Camera Movement for Minimally Invasive Surgery

Authors: Alaa Eldin Abdelaal, Nancy Hong, Apeksha Avinash, Divya Budihal, Maram Sakr, Gregory D. Hager, Septimiu E. Salcudean

Abstract: We propose a new method for six-degree-of-freedom (6-DoF) autonomous camera movement for minimally invasive surgery, which, unlike previous methods, takes into account both the position and orientation information from structures in the surgical scene. In addition to locating the camera for a good view of the manipulated object, our autonomous camera takes into account workspace constraints, inclu… ▽ More We propose a new method for six-degree-of-freedom (6-DoF) autonomous camera movement for minimally invasive surgery, which, unlike previous methods, takes into account both the position and orientation information from structures in the surgical scene. In addition to locating the camera for a good view of the manipulated object, our autonomous camera takes into account workspace constraints, including the horizon and safety constraints. We developed a simulation environment to test our method on the "wire chaser" surgical training task from validated training curricula in conventional laparoscopy and robot-assisted surgery. Furthermore, we propose, for the first time, the application of the proposed autonomous camera method in video-based surgical skill assessment, an area where videos are typically recorded using fixed cameras. In a study with N=30 human subjects, we show that video examination of the autonomous camera view as it tracks the ring motion over the wire leads to more accurate user error (ring touching the wire) detection than when using a fixed camera view, or camera movement with a fixed orientation. Our preliminary work suggests that there are potential benefits to autonomous camera positioning informed by scene orientation, and this can direct designers of automated endoscopes and surgical robotic systems, especially when using chip-on-tip cameras that can be wristed for 6-DoF motion. △ Less

Submitted 4 December, 2020; originally announced December 2020.