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Factory: Fast Contact for Robotic Assembly
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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.
Forward citations
Cited by 5 Pith papers
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FORGE-plus: Force-Budgeted Recovery for Contact-Rich Assembly with a Frozen LLM Supervisor
With a hidden per-episode breaking force, an LLM-set force ceiling plus force-signature recovery achieves 256/256 clean insertions on fragile and robust parts and resolves 40–64% of injected jams in simulation.
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Pipette: An Embodied Simulation Platform, Benchmark, and Data-Efficient Augmentation Framework for Wet-Lab Robotics
With 30 demos per task, simulation replay augmentation raises SmolVLA wet-lab success from ~44% to ~75% and modestly helps π0, while ACT remains competitive as a non-VLA baseline.
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BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly
BiAssemble predicts bimanual grasp and assembly actions for geometric reassembly of fractured objects via point-level collaborative affordance, and reports simulation gains over baselines plus a real-world benchmark.
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Fabrica: Dual-Arm Assembly of General Multi-Part Objects via Integrated Planning and Learning
A dual-arm robotic system combining hierarchical planning with equivariant residual RL policies demonstrates multi-part assembly of five-to-nine-part objects, with strong step-level but weaker end-to-end real-world success.
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Gaussian Process-Based Active Exploration Strategies in Vision and Touch
A robot arm uses Gaussian Process Distance Fields to fuse RGBD vision and tactile contacts, actively choosing next views and touch points to reduce shape uncertainty, while material classification remains near chance.
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