IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality
Bingjie Tang, Michael A. Lin, Iretiayo Akinola, Ankur Handa, Gaurav S., Sukhatme, Fabio Ramos, Dieter Fox, Yashraj Narang

TL;DR
IndustReal introduces novel algorithms and tools that enable transfer of contact-rich robotic assembly tasks from simulation to real-world applications, demonstrating effective policy transfer and repeatable performance.
Contribution
The paper presents new simulation-aware RL algorithms, a policy-level action integrator, and a complete robotic assembly system for contact-rich tasks, facilitating simulation-to-reality transfer.
Findings
Successful transfer of policies from simulation to real robots.
Robots perform contact-rich assembly tasks with high precision.
Tools provided for reproducibility and further research.
Abstract
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…
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Taxonomy
TopicsRobot Manipulation and Learning · Manufacturing Process and Optimization · Reinforcement Learning in Robotics
