Event-Aligned Visual Action Reasoning for World Action Models
cs.CV, cs.RO
Submitted: 2026-10-07
Updated: 2026-10-07
Comments: Project Page: https://xiaomeng-yang.github.io/Event-aligned-WAM/
Project page: https://xiaomeng-yang.github.io/Event-aligned-WAM
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Cosmos 3: Omnimodal World Models for Physical AI
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- SKIP: Sparse Keyframe Interpolation Paradigm for Efficient Embodied World Models
- Causal World Modeling for Robot Control
- DynamicWAM: Dual-Path Motion Conditioning for World-Action Models in Dynamic Manipulation
- SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
- Closing the Loop in Humanoid VLA: Persistent 3D Object Tokens for Verifiable Loco-Manipulation
- Gemini Robotics: Bringing AI into the Physical World
- Wan: Open and Advanced Large-Scale Video Generative Models
- From Foundation to Application: Improving VLA Models in Practice
- Next Forcing: Causal World Modeling with Multi-Chunk Prediction
- ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning
- World Action Models are Zero-shot Policies
- Fast-WAM: Do World Action Models Need Test-time Future Imagination?
- ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?
- X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model
Related papers
- Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
- AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
- Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
- MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
- TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
- A Survey on Efficient Vision-Language-Action Models