Abstract
Vascular robotic systems, which have gained popularity in clinic, provide a platform for potentially semi-automated surgery. Reinforcement learning (RL) is a appealing skill-learning method to facilitate automatic instrument delivery. However, the notorious sample inefficiency of RL has limited its application in this domain. To address this issue, this paper proposes a novel RL framework, Distributed Reinforcement learning with Adaptive Conservatism (DRAC), that learns manipulation skills with a modest amount of interactions. DRAC pretrains skills from rule-based interactions before online fine-tuning to utilize prior knowledge and improve sample efficiency. Moreover, DRAC uses adaptive conservatism to explore safely during online fine-tuning and a distributed structure to shorten training time. Experiments in a pre-clinical environment demonstrate that DRAC can deliver guidewire to the target with less dangerous exploration and better performance than prior methods (success rate of 96.00% and mean backward steps of 9.54) within 20k interactions. These results indicate that the proposed algorithm is promising to learn skills for vascular robotic systems.
This work was supported in part by the National Natural Science Foundation of China under Grant 62003343, Grant 62222316, Grant U1913601, Grant 62073325, Grant U20A20224, and Grant U1913210; in part by the Beijing Natural Science Foundation under Grant M22008; in part by the Youth Innovation Promotion Association of Chinese Academy of Sciences (CAS) under Grant 2020140; in part by the CIE-Tencent Robotics X Rhino-Bird Focused Research Program.
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Li, H. et al. (2024). Effective Skill Learning on Vascular Robotic Systems: Combining Offline and Online Reinforcement Learning. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Communications in Computer and Information Science, vol 1969. Springer, Singapore. https://doi.org/10.1007/978-981-99-8184-7_3
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