  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 .
