Factory: Fast Contact for Robotic Assembly
May 07, 2022 ยท Declared Dead ยท ๐ Robotics: Science and Systems
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Yashraj Narang, Kier Storey, Iretiayo Akinola, Miles Macklin, Philipp Reist, Lukasz Wawrzyniak, Yunrong Guo, Adam Moravanszky, Gavriel State, Michelle Lu, Ankur Handa, Dieter Fox
arXiv ID
2205.03532
Category
cs.RO: Robotics
Cross-listed
cs.GR,
cs.LG
Citations
103
Venue
Robotics: Science and Systems
Last Checked
3 months ago
Abstract
Robotic assembly is one of the oldest and most challenging applications of robotics. In other areas of robotics, such as perception and grasping, simulation has rapidly accelerated research progress, particularly when combined with modern deep learning. However, accurately, efficiently, and robustly simulating the range of contact-rich interactions in assembly remains a longstanding challenge. In this work, we present Factory, a set of physics simulation methods and robot learning tools for such applications. We achieve real-time or faster simulation of a wide range of contact-rich scenes, including simultaneous simulation of 1000 nut-and-bolt interactions. We provide $60$ carefully-designed part models, 3 robotic assembly environments, and 7 robot controllers for training and testing virtual robots. Finally, we train and evaluate proof-of-concept reinforcement learning policies for nut-and-bolt assembly. We aim for Factory to open the doors to using simulation for robotic assembly, as well as many other contact-rich applications in robotics. Please see https://sites.google.com/nvidia.com/factory for supplementary content, including videos.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
๐ Similar Papers
In the same crypt โ Robotics
๐
๐
Old Age
R.I.P.
๐ป
Ghosted
ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras
R.I.P.
๐ป
Ghosted
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
R.I.P.
๐ป
Ghosted
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
R.I.P.
๐ป
Ghosted
Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World
R.I.P.
๐ป
Ghosted
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Died the same way โ ๐ป Ghosted
R.I.P.
๐ป
Ghosted
Language Models are Few-Shot Learners
R.I.P.
๐ป
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
๐ป
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
๐ป
Ghosted