Robots learn delicate assembly tasks through simulation training
NVIDIA Blog: Practice Makes Precise: At CoRL, NVIDIA and Partners Reveal How Robots Rehearse Delicate Work in Simulation
Simulation, which has long been the proving ground for robots learning to walk, roll and hop, is now helping researchers teach robots tasks that require a finer touch: aligning gears, connecting cables and assembling small components. The shift from locomotion to manipulation is taking center stage at this week’s Conference on Robot Learning (CoRL) in Austin, Texas, alongside the rise of agentic robotics — a new paradigm that integrates frontier agents to create interpretable code for physical
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Robots are learning to perform delicate manipulation tasks like assembling components and connecting cables through simulation-based training, a shift from previous focus on locomotion. A world action model trained on diverse robot and human data can perform complex tasks with high precision and transfer skills between different robot types with minimal demonstrations. Simulation frameworks with accurate physics modeling allow researchers to train thousands of robots in parallel before deploying them on physical hardware, enabling the precision and adaptability needed for industrial deployment. This represents a move toward agentic robotics, where AI agents can autonomously sequence skills and recover from errors during task execution.
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