AR-WAM: A Visual-Conditioned Agent-Ready World Action Model for Robotic Manipulation

arXiv:2609.23578 · cs.RO · Submitted 2026-09-20 · Read on arXiv

cs.RO

Submitted: 2026-09-20

Updated: 2026-10-02

Terminology

• Grounding — 27× in this paper · explained in 3 episodes
• Visual Grounding — 10× in this paper · explained in 2 episodes
• Benchmark — 7× in this paper · explained in 2 episodes
• Model-Agnostic — 6× in this paper · explained in 2 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 4× in this paper
• Affordance — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Conference on Robot Learning — 3× in this paper
• Trace — 3× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• KV cache — 2× in this paper · explained in 3 episodes
• Steering — 2× in this paper · explained in 2 episodes
• VLA Models — 2× in this paper · explained in 3 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptive Planning — 1× in this paper · explained in 1 episode
• Agent Harness — 1× in this paper · explained in 2 episodes
• Continual Learning — 1× in this paper · explained in 2 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Decode — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Entanglement — 1× in this paper · explained in 4 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• International Conference on Machine Learning — 1× in this paper
• Latent Reasoning — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes
• Temporal Reasoning — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes
• Vision-language models — 1× in this paper · explained in 2 episodes

Sources

• RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
• RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design
• $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
• GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
• LiLa-WAM: Lightweight Latent Reasoning World-Action Model for Robotic Manipulation
• LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies
• Being-H0.7: A Latent World-Action Model from Egocentric Videos
• Motus: A Unified Latent Action World Model
• Fast-WAM: Do World Action Models Need Test-time Future Imagination?
• DINOv3
• VP-VLA: Visual Prompting as an Interface for Vision-Language-Action Models
• Point What You Mean: Visually Grounded Instruction Policy
• MaskWAM: Unifying Mask Prompting and Prediction for World-Action Models
• DexGraspVLA: A Vision-Language-Action Framework Towards General Dexterous Grasping
• Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents
• Goal2Skill: Long-Horizon Manipulation with Adaptive Planning and Reflection
• Cortex: A Bidirectionally Aligned Embodied Agent Framework for Long-horizon Manipulation
• Do You Need Proprioceptive States in Visuomotor Policies?

Related papers

• FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
• 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
• HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
• APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
• Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving