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
@article{arxiv.2305.12127,
title = {DexPBT: Scaling up Dexterous Manipulation for Hand-Arm Systems with Population Based Training},
author = {Aleksei Petrenko and Arthur Allshire and Gavriel State and Ankur Handa and Viktor Makoviychuk},
journal= {arXiv preprint arXiv:2305.12127},
year = {2023}
}