ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity
arxiv(2024)
摘要
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
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