MVG-WAM: Multiple View Geometry-Aware World-Action Modeling for Robotic Manipulation

arXiv:2609.37793 · cs.RO · Submitted 2026-09-29 · Read on arXiv

cs.RO

Submitted: 2026-09-29

Updated: 2026-09-29

Project page: https://bobc-123.github.io/MVG-WAM

Terminology

• Benchmark — 7× in this paper · explained in 2 episodes
• Grounding — 7× in this paper · explained in 3 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Transformer — 4× in this paper · explained in 2 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• World Model — 3× in this paper · explained in 7 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Calibration — 2× in this paper · explained in 7 episodes
• Distribution Shift — 2× in this paper · explained in 6 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Latent Dynamics — 1× in this paper · explained in 2 episodes
• Latent Reasoning — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes

Sources

• World Action Models are Zero-shot Policies
• DiT4DiT: Jointly Modeling Video Dynamics and Actions for Generalizable Robot Control
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• GeoAware-VLA: Implicit Geometry Aware Vision-Language-Action Model
• ROCKET: Residual-Oriented Multi-Layer Alignment for Spatially-Aware Vision-Language-Action Models
• SG-WAM: Text-Grounded and Spatial-aware Semantic Guidance for World-Action Models
• GeoSem-WAM: Geometry- and Semantic-Aware World Action Models
• Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising
• WAM4D: Fast 4D World Action Model via Spatial Register Tokens
• Learning 4D Geometric Priors for Inference-Efficient World Action Models
• SG-WAM: Self-Guided World Modeling in Geometry-Aware Policy Space
• ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning
• StarVLA- alpha: Reducing Complexity in Vision-Language-Action Systems
• LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation
• Wan: Open and Advanced Large-Scale Video Generative Models
• Do World Action Models Generalize Better than VLAs? A Robustness Study
• XPolicyLab: A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment

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