Sim-to-real Deep Reinforcement Learning (DRL) has shown promising in subtasks automation for surgical robotic systems, since it allows to safely perform all the trial and error attempts needed to learn the optimal control policy. However, a realistic simulation environment is essential to guarantee direct transfer of the learnt policy from the simulated to the real system. In this work, we introduce UnityFlexML, an open-source framework providing support for soft bodies simulation and state-of-the-art DRL methods. We demonstrate that a DRL agent can be successfully trained within UnityFlexML to manipulate deformable fat tissues for tumor exposure during a nephrectomy procedure. Furthermore, we show that the learned policy can be directly deployed on the da Vinci Research Kit, which is able to execute the trajectories generated by the DRL agent. The proposed framework represents an essential component for the development of autonomous robotic systems, where the interaction with the deformable anatomical environment is involved.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    UnityFlexML: Training Reinforcement Learning Agents in a Simulated Surgical Environment



    Erscheinungsdatum :

    01.01.2020


    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629