ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914768827187200 |
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| author | Yu, Qinxi Moghani, Masoud Dharmarajan, Karthik Schorp, Vincent Panitch, William Chung-Ho Liu, Jingzhou Hari, Kush Huang, Huang Mittal, Mayank Goldberg, Ken Garg, Animesh |
| author_facet | Yu, Qinxi Moghani, Masoud Dharmarajan, Karthik Schorp, Vincent Panitch, William Chung-Ho Liu, Jingzhou Hari, Kush Huang, Huang Mittal, Mayank Goldberg, Ken Garg, Animesh |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16027 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity Yu, Qinxi Moghani, Masoud Dharmarajan, Karthik Schorp, Vincent Panitch, William Chung-Ho Liu, Jingzhou Hari, Kush Huang, Huang Mittal, Mayank Goldberg, Ken Garg, Animesh Robotics 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 |
| title | ORBIT-Surgical: An Open-Simulation Framework for Learning Surgical Augmented Dexterity |
| topic | Robotics |
| url | https://arxiv.org/abs/2404.16027 |