← Back to all news
arXiv AI By Tuo An, Jindou Jia, Gen Li, Jingliang Li, Chuhao Zhou, Pengfei Liu, Bofan Lyu, Jiaqi Bai, Xinying Guo, Geng Li, Jianfei Yang

Feedback World Model Enables Precise Guidance of Diffusion Policy

Read the original on arXiv AI →

The paper introduces a feedback world model that updates predictions online using the robot’s observed next state after each action, thereby closing the loop between prediction and observation. This lightweight feedback state corrects future predictions without extra training or parameter updates, and the authors provide convergence guarantees under bounded dynamics residuals. Experiments on LIBERO‑Plus, Robomimic, and real‑world tasks show significant reductions in prediction error (up to 76.4%) and improved policy performance, even when applied to pretrained foundation models like Fast‑WAM‑Joint.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Computer Vision
Sep 16

World-Action Models for Robot Learning and Control: A Survey

The survey "World-Action Models for Robot Learning and Control" reviews recent advances in coupling future world prediction with executable action generation for robots in open environments. It clarifies the scope of World-Action Models (WAMs) relative to conventional world models, model-based RL, and Vision‑Language‑Action policies, and organizes existing methods through a unified taxonomy covering representations, transition modeling, action interfaces, architectures, training pipelines, data modalities, and scaling strategies. The paper also surveys applications in manipulation, navigation, and autonomous driving, summarizes datasets, benchmarks, and metrics, and discusses key challenges such as action alignment, spatial consistency, long‑horizon memory, and efficient inference.

By Zuxing Lu, Hongjia Zhai, Guanzhi Wang, Huajian Zeng, Jiaqi Yang, Jingyu Liu, Lei Cheng, Yuantai Zhang, Yuheng Qiu, Zezhou Cheng, Ivan Laptev, Danfei Xu, Benjamin Riviere, Giuseppe Loianno, Eric Xing, Xingxing Zuo
agentsdiffusionreinforcement-learningroboticsmultimodalbenchmarkssafety
More like this →
arXiv AI
Aug 3

FBFM: A Training-Free Asynchronous Feedback Mechanism for Flow-Matching in World-Action Models Execution

arXiv:2607. 29235v1 Announce Type: cross Abstract: Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands repeated re-grounding in real observations--not recursive rollout.

By Peize Li, Ruimeng Zhang, Ru Zhang, Cong Huang, Kai Chen, Shanghang Zhang
diffusionroboticsefficiency
More like this →