AD-WM: Action-Discriminative World Models for Counterfactual Model Predictive Control
AD-WM improves counterfactual planning by preserving action information, raising hard-start success from 3.7% to 52%.
AD-WM is an action-discriminative joint-embedding world model for counterfactual model predictive control. It pairs residual latent dynamics with action-recovery regularization from inverse dynamics and a normalized recovery objective; auxiliary heads are dropped at test time. On OGBench-Cube, hard-start success rises from 3.7% to 52.0% versus a matched LeWM baseline, with mean gains in four of five simulation environments. With a frozen V-JEPA 2 encoder and matched DROID post-training, zero-shot Franka pick-and-place success increases from 42.2% to 71.1%.
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