Robotic assembly is a longstanding challenge, requiring contact-rich interaction and high precision and accuracy. Many applications also require adaptivity to diverse parts, poses, and environments, as well as low cycle times. In other areas of robotics, simulation is a powerful tool to develop algorithms, generate datasets, and train agents. However, simulation has had a more limited impact on assembly. We present IndustReal, a set of algorithms, systems, and tools that solve assembly tasks in simulation with reinforcement learning (RL) and successfully achieve policy transfer to the real world. Specifically, we propose 1) simulation-aware policy updates, 2) signed-distance-field rewards, and 3) sampling-based curricula for robotic RL agents. We use these algorithms to enable robots to solve contact-rich pick, place, and insertion tasks in simulation. We then propose 4) a policy-level action integrator to minimize error at policy deployment time. We build and demonstrate a real-world robotic assembly system that uses the trained policies and action integrator to achieve repeatable performance in the real world. Finally, we present hardware and software tools that allow other researchers to reproduce our system and results. For videos and additional details, please see http://sites.google.com/nvidia.com/industreal .

翻译：机器人装配是一项长期挑战，需要高接触交互以及高精度与高准确性。许多应用还需要适应多样化的零件、位姿与环境，并实现低循环时间。在机器人学的其他领域中，仿真是开发算法、生成数据集和训练智能体的强大工具。然而，仿真的影响在装配领域较为有限。我们提出IndustReal——一套能够通过强化学习在仿真中解决装配任务并成功实现策略向现实世界迁移的算法、系统与工具。具体而言，我们提出：1）仿真感知的策略更新，2）符号距离场奖励，以及3）基于采样的机器人强化学习智能体课程。利用这些算法，我们使机器人能够在仿真中完成高接触的拾取、放置与插入任务。随后我们提出4）一种策略级动作集成器，用于在策略部署阶段最小化误差。我们构建并展示了一个真实世界机器人装配系统，该系统利用训练好的策略与动作集成器，在现实环境中实现了可重复的性能。最后，我们提供硬件与软件工具，使其他研究者能够复现我们的系统与结果。视频及更多详情请参见http://sites.google.com/nvidia.com/industreal。