SutureBot实现机器人自主缝合全流程，提升精度并提供可复现基准。
SutureBot: A Precision Framework & Benchmark For Autonomous End-to-End Suturing
- 基于目标条件框架优化针尖定位，提高命中精度59%~74%
- 在dVRK上完成拾针、穿刺、打结全流程自主缝合
- 公开1890条高保真示范数据，适配视觉-语言-动作模型研究
机器人缝合是典型的长时程精细操作任务，需协调持针、精准穿刺和牢固打结。尽管已有大量端到端自主研究，但物理硬件上的完整自主缝合仍未实现。本文提出SutureBot：一个基于da Vinci Research Kit（dVRK）的自主缝合基准，涵盖针具抓取、组织穿刺与打结全过程。为确保可重复性，我们发布包含1890次缝合示范的高保真数据集。进一步提出一种目标条件框架，显式优化插入点精度，在对比任务仅基线时提升命中率59%-74%。为建立该任务作为精细模仿学习的基准，我们评估了包括π₀、GR00T N1、OpenVLA-OFT和多任务ACT在内的先进视觉-语言-动作（VLA）模型，并均引入高层任务预测策略。本工作推动手术机器人自主化的关键进展，支持以精度为导向的长时程精细操作策略的可复现开发与评估。数据集已开源：https://huggingface.co/datasets/jchen396/suturebot
原文摘要 · Abstract (English)
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
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