Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning

• URL: http://arxiv.org/abs/2301.00452v2
• Date: Tue, 6 Jun 2023 05:48:09 GMT
• Title: Human-in-the-loop Embodied Intelligence with Interactive Simulation Environment for Surgical Robot Learning
• Authors: Yonghao Long, Wang Wei, Tao Huang, Yuehao Wang, Qi Dou
• Abstract summary: We study human-in-the-loop embodied intelligence with a new interactive simulation platform for surgical robot learning. Specifically, we establish our platform based on our previously released SurRoL simulator with several new features. We showcase the improvement of our simulation environment with the designed new features, and validate effectiveness of incorporating human factors in embodied intelligence.
• Score: 19.390115282150337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Surgical robot automation has attracted increasing research interest over the past decade, expecting its potential to benefit surgeons, nurses and patients. Recently, the learning paradigm of embodied intelligence has demonstrated promising ability to learn good control policies for various complex tasks, where embodied AI simulators play an essential role to facilitate relevant research. However, existing open-sourced simulators for surgical robot are still not sufficiently supporting human interactions through physical input devices, which further limits effective investigations on how the human demonstrations would affect policy learning. In this work, we study human-in-the-loop embodied intelligence with a new interactive simulation platform for surgical robot learning. Specifically, we establish our platform based on our previously released SurRoL simulator with several new features co-developed to allow high-quality human interaction via an input device. We showcase the improvement of our simulation environment with the designed new features, and validate effectiveness of incorporating human factors in embodied intelligence through the use of human demonstrations and reinforcement learning as a representative example. Promising results are obtained in terms of learning efficiency. Lastly, five new surgical robot training tasks are developed and released, with which we hope to pave the way for future research on surgical embodied intelligence. Our learning platform is publicly released and will be continuously updated in the website: https://med-air.github.io/SurRoL.

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