Robot-assisted flexible needle insertion using universal distributional deep reinforcement learning

Xiaoyu Tan, Yonggu Lee, Chin-Boon Chng, Kah-Bin Lim, Chee-Kong Chui

Abstract: Flexible needle insertion is an important minimally invasive surgery approach for biopsy and radio-frequency ablation. This approach can minimize intraoperative trauma and improve postoperative recovery. We propose a new path planning framework using multi-goal deep reinforcement learning to overcome the difficulties in uncertain needle–tissue interactions and enhance the robustness of robot-assisted insertion process.