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

提出一种使用模拟机器人、多指手型器件等智能算法进行巧妙物体操控、亿进行种有挑战性的任务（如再抓取、抓取投掷、物体重新定位等），其中包括引入基于集群的PBT算法来快速扩展深度强化学习的探索能力，相比于端到端学习表现出更强的性能。

DexPBT：基于群体的训练的手臂系统熟练操控扩展