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

Bingjie Tang
University of Southern California
Michael A Lin
Stanford University
Iretiayo A Akinola
Ankur Handa
Gaurav S Sukhatme
University of Southern California, Amazon
Fabio Ramos
Dieter Fox
Yashraj S Narang
Paper Website

Paper ID 39

Session 5. Simulation and Sim2Real

Poster Session Wednesday, July 12

Poster 7

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 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