Atomic-WAM: Execution-Grounded Atomic-Motion Reasoning for World Action Models
cs.RO
Submitted: 2026-10-14
Updated: 2026-10-14
Project page: https://ssj9596.github.io/Atomic-WAM-project
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs
- World Action Models are Zero-shot Policies
- Causal World Modeling for Robot Control
- Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
- Cosmos 3: Omnimodal World Models for Physical AI
- WALL-WM: Carving World Action Modeling at the Event Joints
- DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
- FineVLA: Fine-Grained Instruction Alignment for Steerable Vision-Language-Action Policies
- Steerable Vision-Language-Action Policies for Embodied Reasoning and Hierarchical Control
- ${\pi}_{0.7}$: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- OpenVLA: An Open-Source Vision-Language-Action Model
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- Gemini Robotics: Bringing AI into the Physical World
- DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control
- Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation
- Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model
Related papers
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- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- 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