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

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 the supplementary website.