We introduce a novel approach for learning a complex multi-stage needle pick-and-place manipulation task for surgical applications using Reinforcement Learning without expert demonstrations or explicit curriculum. The proposed method is based on a recursive decomposition of the original task into a sequence of sub-tasks with increasing complexity and utilizes an actor-critic algorithm with deterministic policy output. In this work, exploratory bottlenecks have been used by a human expert as convenient boundary points for partitioning complex tasks into simpler subunits. Our method has successfully learnt a policy for the needle pick-and-place task, whereas the state-of-the-art TD3+HER method is unable to achieve success without the help of expert demonstrations. Comparison results show that our method achieves the highest performance with a 91% average success rate.


    Access

    Download


    Export, share and cite



    Title :

    Learning Needle Pick-And-Place without expert demonstrations



    Publication date :

    2023-04-12


    Remarks:

    IEEE Robotics and Automation Letters (2023) (In press).


    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    LEARNING FLIGHT CONTROL SKILLS BY IMITATING EXPERT DEMONSTRATIONS

    Takase, R. | British Library Conference Proceedings | 2021


    Airship Control using Expert Demonstrations

    Daskiran, Onur / Dogan, Atilla | AIAA | 2016


    DISTRIBUTIONAL EXPERT DEMONSTRATIONS FOR AUTONOMOUS DRIVING

    WU SZU-HAO / JIANG SHU / CAO YU et al. | European Patent Office | 2023

    Free access

    Low Speed Airship Control using Reinforcement Learning and Expert Demonstrations

    Daskiran, Onur / Huff, Brian / Dogan, Atilla | AIAA | 2017