← Back to all news
arXiv Computer Vision By Yijun Yuan, Weicheng Zheng, Weibang Wang, Minghui Qin, Chang Sun, Junhao Huang, Kenan Li, Anmin Liu, Yicheng Yao, Hang Zhao

Representation World Model: Learning States, Transition and Executable Plans in Representation

Read the original on arXiv Computer Vision →

The Representation World Model (RWM) learns states, transitions, and executable plans directly within a representation space, bypassing traditional explicit dynamics models and action-space search. It uses inverse-dynamics supervision along latent paths to shape the representation geometry, enabling direct planning by constructing a latent path between current and goal states and recovering actions via inverse dynamics. Experiments on continuous-control benchmarks and robotic manipulation tasks demonstrate RWM’s effectiveness and potential for complex embodied control.

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 Computer Vision.

Related stories

Hugging Face Trending Papers
Sep 3

Toward Physically Grounded JEPA World Models for Goal-Conditioned Robotic Planning

The paper presents an end‑to‑end JEPA world model that enhances latent prediction with inverse dynamics and state alignment to better support goal‑conditioned robotic planning. By incorporating inverse dynamics, the model prevents latent collapse and encodes action information, while state alignment ties consecutive latent states to their physical configurations and motions. Experiments on four benchmark tasks show the model achieves top success rates on TwoRoom, PushT, and OGBench‑Cube, and its state alignment consistently improves planning performance over inverse dynamics alone.

roboticsbenchmarkssafety
More like this →
arXiv AI
Sep 3

What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?

The paper investigates Joint-Embedding Predictive World Models (JEPA-WMs), a class of methods that perform planning in a learned representation space rather than raw input space. It systematically studies how model architecture, training objectives, and planning algorithms influence success across simulated and real‑world robotic tasks, and proposes a JEPA-WM variant that surpasses established baselines in navigation and manipulation. The authors provide code, data, and checkpoints for reproducibility.

By Basile Terver, Tsung-Yen Yang, Jean Ponce, Adrien Bardes, Yann LeCun
ragagentsrobotics
More like this →