Three Paths to Robot Training Data: Simulation, Real-Robot Collection, and Autonomous Patrol

Superb AI describes three complementary methods for building robot behavior datasets: generating demonstrations in NVIDIA Isaac Sim, collecting teleoperated demonstrations with physical robots, and allowing a quadruped robot to gather camera data autonomously while patrolling. Simulation can produce large, varied datasets by changing environments, tasks, and objects, addressing the limited generalization of an earlier GR00T model trained on 1,000 episodes involving only a red cube in a sparse scene. Physical-robot collection has yielded 2,500 episodes combining synchronized head and wrist camera footage, robot joint states, and language instructions, with quality controls that discard incomplete or poorly visible recordings. The company preserves differences between commanded and actual joint states because the robot’s safety-limited low-level controller creates those differences during both training and inference. In the autonomous patrol demonstration, locomotion is learned through reinforcement learning, while the arm’s bottle-and-cup manipulation is computed with cuRobo motion planning rather than learned. Superb AI emphasizes that combining simulated and real-world data requires aligning their distributions, including by applying comparable physical constraints such as joint-velocity limits in simulation, and plans to expand its robot behavior data alongside its existing industrial visual-data collection.

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