ORB: Operating Room Bot, Automating Operating Room Logistics through Mobile Manipulation
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918144228982784 |
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| author | Qiu, Jinkai Kim, Yungjun Sethia, Gaurav Agarwal, Tanmay Ghodasara, Siddharth Erickson, Zackory Ichnowski, Jeffrey |
| author_facet | Qiu, Jinkai Kim, Yungjun Sethia, Gaurav Agarwal, Tanmay Ghodasara, Siddharth Erickson, Zackory Ichnowski, Jeffrey |
| contents | Efficiently delivering items to an ongoing surgery in a hospital operating room can be a matter of life or death. In modern hospital settings, delivery robots have successfully transported bulk items between rooms and floors. However, automating item-level operating room logistics presents unique challenges in perception, efficiency, and maintaining sterility. We propose the Operating Room Bot (ORB), a robot framework to automate logistics tasks in hospital operating rooms (OR). ORB leverages a robust, hierarchical behavior tree (BT) architecture to integrate diverse functionalities of object recognition, scene interpretation, and GPU-accelerated motion planning. The contributions of this paper include: (1) a modular software architecture facilitating robust mobile manipulation through behavior trees; (2) a novel real-time object recognition pipeline integrating YOLOv7, Segment Anything Model 2 (SAM2), and Grounded DINO; (3) the adaptation of the cuRobo parallelized trajectory optimization framework to real-time, collision-free mobile manipulation; and (4) empirical validation demonstrating an 80% success rate in OR supply retrieval and a 96% success rate in restocking operations. These contributions establish ORB as a reliable and adaptable system for autonomous OR logistics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15600 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | ORB: Operating Room Bot, Automating Operating Room Logistics through Mobile Manipulation Qiu, Jinkai Kim, Yungjun Sethia, Gaurav Agarwal, Tanmay Ghodasara, Siddharth Erickson, Zackory Ichnowski, Jeffrey Robotics Efficiently delivering items to an ongoing surgery in a hospital operating room can be a matter of life or death. In modern hospital settings, delivery robots have successfully transported bulk items between rooms and floors. However, automating item-level operating room logistics presents unique challenges in perception, efficiency, and maintaining sterility. We propose the Operating Room Bot (ORB), a robot framework to automate logistics tasks in hospital operating rooms (OR). ORB leverages a robust, hierarchical behavior tree (BT) architecture to integrate diverse functionalities of object recognition, scene interpretation, and GPU-accelerated motion planning. The contributions of this paper include: (1) a modular software architecture facilitating robust mobile manipulation through behavior trees; (2) a novel real-time object recognition pipeline integrating YOLOv7, Segment Anything Model 2 (SAM2), and Grounded DINO; (3) the adaptation of the cuRobo parallelized trajectory optimization framework to real-time, collision-free mobile manipulation; and (4) empirical validation demonstrating an 80% success rate in OR supply retrieval and a 96% success rate in restocking operations. These contributions establish ORB as a reliable and adaptable system for autonomous OR logistics. |
| title | ORB: Operating Room Bot, Automating Operating Room Logistics through Mobile Manipulation |
| topic | Robotics |
| url | https://arxiv.org/abs/2509.15600 |