arXiv AI
Aug 10

TaskSense: Focusing on What Matters in World Models

arXiv:2608. 06544v1 Announce Type: new Abstract: World models for visual control typically learn compact latent states by reconstructing observations, implicitly encouraging representations to preserve information across the entire visual input.

By SM Mazharul Islam, Manfred Huber
arXiv Machine Learning
Oct 3

JEPA-Bisim: Learning Robust Visual Representations for Planning with Joint-Embedding Predictive World Models

JEPA-Bisim introduces a bisimulation encoder to joint-embedding predictive world models, ensuring that states with similar transition dynamics are mapped to nearby latent representations while suppressing irrelevant slow features such as background changes and distractors. The approach improves robustness on navigation (PointMaze) and manipulation (PushT) tasks under varied test-time visual conditions, achieving up to tenfold smaller latent spaces than DINO-WM. It remains effective across different pre-trained visual encoders, including DINOv2, SimDINOv2, and iBOT.

By Leonardo F. Toso, Davit Shadunts, Yunyang Lu, Gloria Geng, Nihal Sharma, Donglin Zhan, Nam H. Nguyen, James Anderson
arXiv Machine Learning
Oct 8

SearchWorld: Spatial Value-Grounded Imagination for UAV Object Search via World Models

SearchWorld introduces a recurrent state‑space world model that couples explicit spatial memory with value‑guided imagination for UAV object search. The model maintains bird’s‑eye‑view exploration and obstacle memory, decodes a task‑aware spatial value layer, and uses a cognition‑action network to improve policy through imagined rollouts without a separate critic. Experiments on UAV‑ON show a 23.8% success rate, surpassing the best published agent (19.5%) and achieving 35.5% oracle success while remaining robust on unseen scenes.

By Yatai Ji, Zhengqiu Zhu, Yong Zhao, Yue Hu, Fanglong Yao, Chen Gao, Pengfei Zhu, Quanjun Yin