[2603.26720] SutureAgent: Learning Surgical Trajectories via Goal-conditioned Offline RL in Pixel Space

Computer Science > Robotics arXiv:2603.26720 (cs) [Submitted on 19 Mar 2026] Title:SutureAgent: Learning Surgical Trajectories via Goal-conditioned Offline RL in Pixel Space Authors:Huanrong Liu, Chunlin Tian, Tongyu Jia, Tailai Zhou, Qin Liu, Yu Gao, Yutong Ban, Yun Gu, Guy Rosman, Xin Ma, Qingbiao Li View a PDF of the paper titled SutureAgent: Learning Surgical Trajectories via Goal-conditioned Offline RL in Pixel Space, by Huanrong Liu and 10 other authors View PDF HTML (experimental) Abstract:Predicting surgical needle trajectories from endoscopic video is critical for robot-assisted suturing, enabling anticipatory planning, real-time guidance, and safer motion execution. Existing methods that directly learn motion distributions from visual observations tend to overlook the sequential dependency among adjacent motion steps. Moreover, sparse waypoint annotations often fail to provide sufficient supervision, further increasing the difficulty of supervised or imitation learning methods. To address these challenges, we formulate image-based needle trajectory prediction as a sequential decision-making problem, in which the needle tip is treated as an agent that moves step by step in pixel space. This formulation naturally captures the continuity of needle motion and enables the explicit modeling of physically plausible pixel-wise state transitions over time. From this perspective, we propose SutureAgent, a goal-conditioned offline reinforcement learning framework that lever...