CAG-WAM: Causal Action-Gain World Action Models for Robot Manipulation
cs.RO
Submitted: 2026-10-10
Updated: 2026-10-10
Sources
- World Simulation with Video Foundation Models for Physical AI
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- UniVLA: Learning to Act Anywhere with Task-centric Latent Actions
- Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models
- ABot-M0.5: Unified Mobility-and-Manipulation World Action Model
- Towards Fusing Point Cloud and Visual Representations for Imitation Learning
- GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions
- FlowMo-WM: A World Model with Object Momentum and Hidden Ambient Drift
- 3D Diffuser Actor: Policy Diffusion with 3D Scene Representations
- OpenVLA: An Open-Source Vision-Language-Action Model
- Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
- Causal World Modeling for Robot Control
- Unified Video Action Model
- Video Generators are Robot Policies
- STORM: Search-Guided Generative World Models for Robotic Manipulation
- OA-WAM: Object-Addressable World Action Model for Robust Robot Manipulation
- RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots
- SelfWAM: A Self-Grounded Unified World Action Model for Fast Robot Control
- FACT: Failure-Aware Causal Training for World-Action Models
- SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model
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- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies