ORB: Operating Room Bot, Automating Operating Room Logistics through Mobile Manipulation

2025-09-19

AI Summary

To address challenges in operating room (OR) item-level logistics automation—including low perceptual accuracy, insufficient operational efficiency, and difficulty maintaining sterility—this paper proposes a mobile manipulation robot system specifically designed for sterile environments. Methodologically, we introduce a hierarchical behavior-tree-driven modular architecture, integrating YOLOv7, SAM2, and Grounded DINO to construct a multi-granularity object recognition and scene understanding pipeline; real-time trajectory optimization and collision checking are accelerated via cuRobo on GPU. Our key contribution is the first deep adaptation of embodied intelligence techniques to dynamic OR sterility constraints. Evaluated in realistic surgical settings, the system achieves 80% pick success rate and 96% restocking success rate, significantly enhancing supply delivery reliability and intraoperative safety. This work establishes a reusable technical paradigm for deploying medical robots in high-sensitivity clinical environments.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Natural Language Processing: Safety and Robustness

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Automating item-level logistics in operating rooms
• Addressing perception and sterility challenges in OR
• Enabling real-time collision-free mobile manipulation

Innovation

• Hierarchical behavior tree architecture
• Real-time object recognition pipeline
• GPU-accelerated motion planning