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 $\pi_0$, 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: \href{https://huggingface.co/datasets/jchen396/suturebot}{Hugging Face}

This paper describes a novel tissue positioning system with an integrated suturing robot and demonstrates its ability to perform semi-automatic anastomoses of synthetic blood vessels. We began with a finite element analysis-based design consideration for achieving adequate grasping of blood vessels to demonstrate robust performance under expected clinical forces. We then conducted standardized positioning tests to measure the repeatability of the system and incorporated a high-resolution optical coherence tomography (OCT) fiber imaging sensor within the tip of the suturing tool to provide position feedback of the robot during a suturing task. Using the microvascular positioner and OCT sensor, the system performed semi-automatic suturing of synthetic 5 mm diameter blood vessels ($\mathrm{N}=4$), and the suture quality was evaluated for consistency in spacing, bite depth, percent lumen reduction, and maximum suture strength. The system completed the task in an average time of 31.75 minutes. The samples had zero missed stitches, average spacing of 1.64 mm, an average bite depth of 2.14 mm, an average lumen reduction of 57.98%, and an average suture strength of 3.13 N.