Learning to Steer in Pure Simulation Enhances Real-World Dexterous Policies
chris_j_paxton · x · 2026-07-03
An embodied AI research paper proposes improving real-world dexterous policies by "learning to steer in pure simulation." By training the policy's online adjustments and error correction in a simulated environment before transferring it to real robots, the approach significantly enhances generalization and success rates in real-world settings.
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