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 this https URL

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

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

In this work, we present and study a training set-up that achieves fast
policy generation for real-world robotic tasks by using massive parallelism on
a single workstation GPU. We analyze and discuss the impact of different
training algorithm components in the massively parallel regime on the final
policy performance and training times. In addition, we present a novel
game-inspired curriculum that is well suited for training with thousands of
simulated robots in parallel. We evaluate the approach by training the
quadrupedal robot ANYmal to walk on challenging terrain. The parallel approach
allows training policies for flat terrain in under four minutes, and in twenty
minutes for uneven terrain. This represents a speedup of multiple orders of
magnitude compared to previous work. Finally, we transfer the policies to the
real robot to validate the approach. We open-source our training code to help
accelerate further research in the field of learned legged locomotion.

本文介绍并研究了一种在单个工作站 GPU 上使用大量并行性实现快速生成真实世界机器人任务策略的训练设置，其中包括不同训练算法组件在大规模并行模式下对最终策略性能和训练时间的影响分析和讨论，同时还介绍了一种新颖的游戏启发课程，适用于数千个模拟机器人的并行训练，并通过将策略转换到真实机器人以验证方法的有效性。