CF-VLA uses a coarse initialization over endpoint velocity followed by single-step refinement to achieve strong performance with low inference steps on CALVIN, LIBERO, and real-robot tasks.
Ladi-wm: A latent diffusion-based world model for predictive manipulation.arXiv preprint arXiv:2505.11528
9 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 9representative citing papers
Attacks can corrupt the latent future trajectory imagined by world-action models in VLA policies, causing failures in oracles like MPC while the reactive policy stays intact.
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.
AstraNav-World unifies diffusion video generation and vision-language action planning in a single bidirectional model that improves trajectory accuracy, success rates, and zero-shot real-world adaptation in embodied navigation.
DreamVLA uses dynamic-region-guided world knowledge prediction, block-wise attention to disentangle information types, and a diffusion transformer for actions, reaching 76.7% success on real robot tasks and 4.44 average length on CALVIN ABC-D.
PhysRAG curates 7K videos from WISA-80K, builds a physical video database, and injects knowledge via learnable queries into a diffusion model to reach SOTA visual quality and physical compliance on PhyGenBench and VBench.
DynaMOMA uses an anchor-based diffusion predictor for temporally consistent grasp trajectories and feeds encoded features to an anticipation-guided whole-body RL policy, reporting strong simulation performance and real-world generalizability for dynamic mobile grasping.
PAIWorld adds explicit geometric cross-view mechanisms and 3D distillation to DiT world models to achieve multi-view 3D consistency in robotic manipulation benchmarks.
WALL-WM introduces event-grounded Vision-Language-Action pretraining that uses semantic events as the atomic unit to address granularity mismatch in world action models and reports state-of-the-art generalization.
citing papers explorer
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CF-VLA: Efficient Coarse-to-Fine Action Generation for Vision-Language-Action Policies
CF-VLA uses a coarse initialization over endpoint velocity followed by single-step refinement to achieve strong performance with low inference steps on CALVIN, LIBERO, and real-robot tasks.
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Attacking the Trusted Imagination: Oracle-Level Integrity Attacks on Imagine-then-Act World Models
Attacks can corrupt the latent future trajectory imagined by world-action models in VLA policies, causing failures in oracles like MPC while the reactive policy stays intact.
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Human Cognition in Machines: A Unified Perspective of World Models
The paper introduces a unified framework for world models that fully incorporates all cognitive functions from Cognitive Architecture Theory, highlights under-researched areas in motivation and meta-cognition, and proposes Epistemic World Models as a new category for scientific discovery agents.
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AstraNav-World: World Model for Foresight Control and Consistency
AstraNav-World unifies diffusion video generation and vision-language action planning in a single bidirectional model that improves trajectory accuracy, success rates, and zero-shot real-world adaptation in embodied navigation.
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DreamVLA: A Vision-Language-Action Model Dreamed with Comprehensive World Knowledge
DreamVLA uses dynamic-region-guided world knowledge prediction, block-wise attention to disentangle information types, and a diffusion transformer for actions, reaching 76.7% success on real robot tasks and 4.44 average length on CALVIN ABC-D.
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PhysRAG: Enhancing Physics-Awareness in Video Generation via Retrieval-Augmented Generation
PhysRAG curates 7K videos from WISA-80K, builds a physical video database, and injects knowledge via learnable queries into a diffusion model to reach SOTA visual quality and physical compliance on PhyGenBench and VBench.
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DynaMOMA: Instantaneous Prediction of Grasp Poses for Mobile Manipulation of Dynamic Objects
DynaMOMA uses an anchor-based diffusion predictor for temporally consistent grasp trajectories and feeds encoded features to an anticipation-guided whole-body RL policy, reporting strong simulation performance and real-world generalizability for dynamic mobile grasping.
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PAIWorld: A 3D-Consistent World Foundation Model for Robotic Manipulation
PAIWorld adds explicit geometric cross-view mechanisms and 3D distillation to DiT world models to achieve multi-view 3D consistency in robotic manipulation benchmarks.
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WALL-WM: Carving World Action Modeling at the Event Joints
WALL-WM introduces event-grounded Vision-Language-Action pretraining that uses semantic events as the atomic unit to address granularity mismatch in world action models and reports state-of-the-art generalization.