Factory: Fast Contact for Robotic Assembly

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 carefullydesigned 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 our website for supplementary content, including videos. 1

VIRDO: Visio-tactile Implicit Representations of Deformable Objects

“…Recent studies have attempted to address challenges in system identification and high computation costs in conventional continuum mechanics [6,7,8,9,10,11,12,13]. To ease the burden of system identification, prior studies infer the physics models' parameters based on high-fidelity physics engines [20,21,22] and simple force-deformation relationship (e.g. Hooke's law) [23].…”

Bridging the Intention–Behavior Gap: Effect of Altruistic Motives on Developers’ Action towards Green Redevelopment of Industrial Brownfields

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“…Traditional methods such as direct trajectory optimization, may struggle to model complex contact dynamics. Due to these difficulties, researchers working on manipulation problems typically focus their efforts on robotic arms with end-effectors that simplify contact handling, such as parallel jaw grippers [5,29,26]. Even though more capable human-like robotic hands have the potential to endow robots with far more advanced manipulation capabilities, this type of end-effector remains relatively unpopular due to the difficulty of controlling high degree-of-freedom (DoF) systems in contact-rich environments.…”