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%.
- Hard-start OGBench-Cube success rises from 3.7% to 52.0% versus LeWM.
- Mean success improves over the baseline in four of five simulated environments.
- CEM-aligned elite regret tracks closed-loop success better than factual prediction error.
- Frozen V-JEPA 2 and DROID post-training lift Franka pick-and-place from 42.2% to 71.1%.
From the original article
1 min read at arxiv.orgLatent world models are typically trained to predict factual transitions, whereas model predictive control (MPC) must compare alternative actions from the same state. A model can therefore achieve low factual prediction error yet poorly distinguish candidate actions. We introduce AD-WM, an action-discriminative joint-embedding world model for counterfactual MPC.
By Jiabin Qiu · arxiv.org
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