Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical Robot
Refereed conference paper presented and published in conference proceedings

Altmetrics Information

Other information
AbstractTask automation of surgical robot has the potentials to improve surgical efficiency. Recent reinforcement learning (RL) based approaches provide scalable solutions to surgical automation, but typically require extensive data collection to solve a task if no prior knowledge is given. This issue is known as the exploration challenge, which can be alleviated by providing expert demonstrations to an RL agent. Yet, how to make effective use of demonstration data to improve exploration efficiency still remains an open challenge. In this work, we introduce Demonstration-guided EXploration (DEX), an efficient reinforcement learning algorithm that aims to overcome the exploration problem with expert demonstrations for surgical automation. To effectively exploit demonstrations, our method estimates expert-like behaviors with higher values to facilitate productive interactions, and adopts non-parametric regression to enable such guidance at states unobserved in demonstration data. Extensive experiments on 10 surgical manipulation tasks from SurRoL, a comprehensive surgical simulation platform, demonstrate significant improvements in the exploration efficiency and task success rates of our method. Moreover, we also deploy the learned policies to the da Vinci Research Kit (dVRK) platform to show the effectiveness on the real robot. Code is available at
All Author(s) ListTao Huang, Kai Chen, Bin Li, Yun-Hui Liu, Qi Dou
Name of ConferenceIEEE International Conference on Robotics and Automation 2023 ICRA
Start Date of Conference29/05/2023
End Date of Conference02/06/2023
Place of ConferenceLondon
Country/Region of ConferenceGreat Britain
Proceedings Title2023 IEEE International Conference on Robotics and Automation (ICRA)
Pages4640 - 4647
LanguagesEnglish-United Kingdom

Last updated on 2023-29-10 at 03:11