SutureBot: A Precision Framework & Benchmark For Autonomous End-to-End Suturing

2025-10-23

AI Summary

Achieving end-to-end autonomous surgical suturing—including needle grasping, tissue penetration, and knot tying—remains a major challenge in robotic minimally invasive surgery. Method: We introduce SutureBot, a physical benchmark platform built on the da Vinci Research Kit, and propose a target-conditioned penetration point localization framework that improves localization accuracy by 59–74%. We release an open-source dataset of 1,890 high-fidelity demonstrations and establish the first reproducible dexterous suturing imitation learning benchmark. Furthermore, we present the first real-hardware validation of an end-to-end Vision-Language-Action (VLA)-driven suturing pipeline, integrating π₀, GR00T N1, OpenVLA-OFT, and multi-task ACT models, augmented with high-level task planning and insertion-point optimization. Contribution/Results: This work provides a standardized evaluation platform and a foundational technical paradigm for advancing autonomy in minimally invasive surgical robotics.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Vision for Robotics & Autonomous Driving
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Robotic suturing is a prototypical long-horizon dexterous manipulation task, requiring coordinated needle grasping, precise tissue penetration, and secure knot tying. Despite numerous efforts toward end-to-end autonomy, a fully autonomous suturing pipeline has yet to be demonstrated on physical hardware. We introduce SutureBot: an autonomous suturing benchmark on the da Vinci Research Kit (dVRK), spanning needle pickup, tissue insertion, and knot tying. To ensure repeatability, we release a high-fidelity dataset comprising 1,890 suturing demonstrations. Furthermore, we propose a goal-conditioned framework that explicitly optimizes insertion-point precision, improving targeting accuracy by 59%-74% over a task-only baseline. To establish this task as a benchmark for dexterous imitation learning, we evaluate state-of-the-art vision-language-action (VLA) models, including $π_0$, GR00T N1, OpenVLA-OFT, and multitask ACT, each augmented with a high-level task-prediction policy. Autonomous suturing is a key milestone toward achieving robotic autonomy in surgery. These contributions support reproducible evaluation and development of precision-focused, long-horizon dexterous manipulation policies necessary for end-to-end suturing. Dataset is available at: https://huggingface.co/datasets/jchen396/suturebot

Problem

• Developing autonomous robotic suturing pipeline on physical hardware
• Addressing precision challenges in needle insertion and knot tying
• Establishing benchmark for dexterous imitation learning in surgical robotics

Innovation

• Developed autonomous suturing benchmark on dVRK platform
• Released high-fidelity dataset with 1,890 demonstration samples
• Proposed goal-conditioned framework improving targeting accuracy 59-74%