ORBIT-Surgical: 手術支援巧緻性学習のためのオープンシミュレーションフレームワーク
ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity
NVIDIA Omniverse上でdVRKとSTARロボット向けに14の手術タスクを提供し、強化学習・模倣学習による手術スキル向上とsim-to-real転移を可能にする物理ベースの手術ロボットシミュレータを開発した。
著者: Qinxi Yu, Masoud Moghani, Karthik Dharmarajan, Vincent Schorp, William Chung-Ho Panitch, Jingzhou Liu, Kush Hari, Huang Huang, Mayank Mittal, Ken Goldberg, Animesh Garg
分類: cs.RO
原文アブストラクト
Physics-based simulations have accelerated progress in robot learning for driving, manipulation, and locomotion. Yet, a fast, accurate, and robust surgical simulation environment remains a challenge. In this paper, we present ORBIT-Surgical, a physics-based surgical robot simulation framework with photorealistic rendering in NVIDIA Omniverse. We provide 14 benchmark surgical tasks for the da Vinci Research Kit (dVRK) and Smart Tissue Autonomous Robot (STAR) which represent common subtasks in surgical training. ORBIT-Surgical leverages GPU parallelization to train reinforcement learning and imitation learning algorithms to facilitate study of robot learning to augment human surgical skills. ORBIT-Surgical also facilitates realistic synthetic data generation for active perception tasks. We demonstrate ORBIT-Surgical sim-to-real transfer of learned policies onto a physical dVRK robot. Project website: orbit-surgical.github.io
