A transformer policy distilled from a privileged RL teacher enables 3.1x faster real-world cube rotation on the ORCA hand using solely joint sensor data by extracting implicit object state from temporal joint patterns.
Domain randomization for transferring deep neural networks from simulation to the real world,
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Learning Robust Dexterous In-Hand Manipulation from Joint Sensors with Proprioceptive Transformer
A transformer policy distilled from a privileged RL teacher enables 3.1x faster real-world cube rotation on the ORCA hand using solely joint sensor data by extracting implicit object state from temporal joint patterns.