Beyond Future Prediction: Denoising as Generative Adaptation for Robot Control

arXiv:2609.28339 · cs.RO · Submitted 2026-09-23 · Read on arXiv

cs.RO

Submitted: 2026-09-23

Updated: 2026-09-23

Project page: https://xmz111.github.io/NowWAM

Terminology

• Adaptation — 22× in this paper · explained in 1 episode
• Robustness — 19× in this paper · explained in 5 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Transformer — 7× in this paper · explained in 2 episodes
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Foundation Model — 6× in this paper · explained in 3 episodes
• Distribution Shift — 4× in this paper · explained in 5 episodes
• World Model — 4× in this paper · explained in 7 episodes
• Conference on Computer Vision and Pattern Recognition — 3× in this paper
• Ablation — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Generative Models — 2× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Flow Matching — 1× in this paper · explained in 7 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes

Sources

• Gen2Act: Human Video Generation in Novel Scenarios enables Generalizable Robot Manipulation
• GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
• $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
• RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
• Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
• AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing
• WorldVLA: Towards Autoregressive Action World Model
• GR-2: A Generative Video-Language-Action Model with Web-Scale Knowledge for Robot Manipulation
• DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA
• LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models
• Vidar: Embodied Video Diffusion Model for Generalist Manipulation
• Video Prediction Policy: A Generalist Robot Policy with Predictive Visual Representations
• RLDX-1 Technical Report
• OpenVLA: An Open-Source Vision-Language-Action Model
• Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
• Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning
• Causal World Modeling for Robot Control
• Video Generators are Robot Policies
• Being-H0.7: A Latent World-Action Model from Egocentric Videos
• RoboCasa: Large-Scale Simulation of Everyday Tasks for Generalist Robots

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