Robotics: Science and Systems XIX

IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality

Bingjie Tang, Michael A Lin, Iretiayo A Akinola, Ankur Handa, Gaurav S Sukhatme, Fabio Ramos, Dieter Fox, Yashraj S Narang


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 our project website at



    AUTHOR    = {Bingjie Tang AND Michael A Lin AND Iretiayo A Akinola AND Ankur Handa AND Gaurav S Sukhatme AND Fabio Ramos AND Dieter Fox AND Yashraj S Narang}, 
    TITLE     = {{IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2023}, 
    ADDRESS   = {Daegu, Republic of Korea}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2023.XIX.039}