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IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality
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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 http://sites.google.com/nvidia.com/industreal .
Forward citations
Cited by 8 Pith papers
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CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation
CoRMA enables within-episode adaptation for contact-rich robotic assembly by inferring semantic contact context with a causal Transformer and force-regime contrastive objective, retaining higher real success than FORG...
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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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AnnotateAnything: Automatic Annotation of 3D Assets for Robot Manipulation
AnnotateAnything converts passive 3D assets into manipulation-ready assets by combining vision-language reasoning for semantics with parallel physics pipelines for executable action annotations such as grasps and arti...
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Multi-Resolution Tactile Imitation Learning for Contact-Rich Robotic Manipulation
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CoRMA: Contrastive RMA for Contact-Rich Meta-Adaptation
CoRMA modifies RMA by replacing raw parameter adaptation with inference of a 6D semantic contact context via a causal Transformer trained with semantic regression and force-regime contrastive loss, yielding higher rea...
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What Matters for Simulation to Online Reinforcement Learning on Real Robots
Sim-to-online RL on three real robots is stabilized by retaining data, warm-starting the replay buffer, and using asymmetric actor-critic updates with a low actor learning rate.
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