This post discusses the advancements in robotic assembly by leveraging NVIDIA Isaac Lab for sim-to-real transfer applications. It highlights the use of zero-shot sim-to-real transfer for gear assembly tasks with the UR10e robot, facilitated by Isaac Lab's flexible, open-source framework and Isaac ROS's collection of AI models. The post explains how reinforcement learning is applied to equip robots with precision in manipulating objects, tackling tasks like motion generation and insertion, and overcoming the reality gap with robust policies. The collaboration between NVIDIA and Universal Robots drives real-world deployment using innovative torque control interfaces.
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Contact-rich simulation in Isaac LabTraining the gear assembly task in Isaac LabNetwork architecture and RL algorithm detailsResultsUR torque control interface enabling sim-to-real transferSim-to-real transfer on UR10e using Isaac ROS and UR torque interfaceGet startedSort: