SutureBot: A Precision Framework & Benchmark For Autonomous End-to-End Suturing
Jesse Haworth, Juo-Tung Chen, Nigel Nelson, Ji Woong Kim, Masoud Moghani, Chelsea Finn, Axel Krieger

TL;DR
SutureBot introduces a comprehensive benchmark and dataset for autonomous robotic suturing, demonstrating significant improvements in precision and establishing a foundation for future dexterous manipulation research.
Contribution
The paper presents SutureBot, a new benchmark and dataset for autonomous suturing, along with a goal-conditioned framework that enhances insertion accuracy, and evaluates state-of-the-art models on this task.
Findings
Insertion-point precision improved by 59-74% with the proposed framework.
High-fidelity dataset of 1,890 suturing demonstrations released.
Benchmark enables reproducible evaluation of dexterous manipulation models.
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 , GR00T…
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Taxonomy
TopicsSoft Robotics and Applications · Robot Manipulation and Learning · Surgical Simulation and Training
