arXiv AI
CAER: Causal Action Effect Reweighting for World Model Training
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arXiv AI
1d ago
CSWAM: Better Causal Semantic Representations for Out-of-Distribution Generalization in World Action Models
The paper introduces CSWAM, a Causal Semantic World Action Model that enhances FastWAM by integrating a causal semantic expert based on V-JEPA 2.1. This expert provides temporally grounded, appearance‑agnostic representations of semantic state changes and motion, leveraging sparse observation history and causal attention to improve action‑only inference. Experiments on simulation and real‑robot tasks show that CSWAM significantly boosts out‑of‑distribution generalization, raising success rates from 10.16% to 45.18% on RoboTwin 2.0 and from 27.5% to 70.0% across real‑robot tasks.
arXiv AI
Sep 2
IMPACT: Attention Is the Interaction Map for Scalable Interaction-Aware World Model Training
arXiv:2609.00161v1 Announce Type: new Abstract: World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet still struggle to model physically plausibl...
arXiv Computer Vision
Aug 25
ReWorld: Representation Learning for World Action Models
arXiv:2606.27504v2 Announce Type: replace Abstract: World Action Models (WAMs) unify future environment prediction with action generation for autonomous driving, yet existing approaches optimize only...
arXiv AI
Jun 11
Making Foresight Actionable: Repurposing Representation Alignment in World Action Models
arXiv:2606. 12217v1 Announce Type: cross Abstract: World Action Models (WAMs) offer a promising route for robot manipulation by using video generation models to model future scene evolution before producing control actions.
arXiv Computer Vision
4d ago
PAVXploreRL: Physical-Action-Visual World Model Reinforcement Learning with Action Exploration
PAVXploreRL introduces a reinforcement learning framework that builds on a pretrained latent world model to explicitly optimize Physical Plausibility, Action Adherence, and Visual Fidelity (PAV) objectives. By combining in‑distribution expert trajectories with noise‑driven out‑of‑distribution action exploration, the method avoids reliance on paired video supervision and improves generalization. Experiments demonstrate a 5.6% average performance gain over pretrained baselines and more reliable policy evaluation with reduced overestimation bias.
arXiv AI
Sep 10
Learning Counterfactual World Models for Embodied Reasoning under Partial Observability
arXiv:2609.05834v1 Announce Type: new Abstract: World models promise a general route to embodied intelligence: learn predictive dynamics once, then reason, plan, and act with them. Increasingly, the...