Robotics: Science and Systems XIX

DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training

Aleksei Petrenko, Arthur Allshire, Gavriel State, Ankur Handa, Viktor Makoviychuk

Abstract:

In this work, we propose algorithms and methods that enable learning dexterous object manipulation using simulated one- or two-armed robots equipped with multi-fingered hand end-effectors. Using a parallel GPU-accelerated physics simulator (Isaac Gym), we implement challenging tasks for these robots, including regrasping, grasp-and-throw, and object reorientation. To solve these problems we introduce a decentralized Population-Based Training (PBT) algorithm that allows us to massively amplify the exploration capabilities of deep reinforcement learning. We find that this method significantly outperforms regular end-to-end learning and is able to discover robust control policies in challenging tasks. Video demonstrations of learned behaviors and the code can be found at https://sites.google.com/view/dexpbt

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Bibtex:

  
@INPROCEEDINGS{Petrenko-RSS-23, 
    AUTHOR    = {Aleksei Petrenko AND Arthur Allshire AND Gavriel State AND Ankur Handa AND Viktor Makoviychuk}, 
    TITLE     = {{DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2023}, 
    ADDRESS   = {Daegu, Republic of Korea}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2023.XIX.037} 
}