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LY-GWM-RoboCasa-Human300: Prediction-Guided Action Selection with a Graph Dynamics Model
We present LY-GWM-RoboCasa-Human300, a policy that combines a Human300-fine-tuned GR00T-N1.5 action generator, an action-conditioned graph dynamics model, and a learned binary state-reward scorer. At inference, the policy samples four action chunks, predicts the future latent features and robot stat
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