The paper introduces EvolvingNav, a system that builds a time‑indexed belief about moving targets in dynamic environments by combining timestamped 3D object histories with a persistence‑relocation model. It uses an event‑driven filter to update beliefs over time, incorporates RGB‑D evidence, and applies a zero‑shot vision‑language controller for action selection. The authors also present EvoWorld‑Bench, a large benchmark of human‑activity‑based scenes, and demonstrate that EvolvingNav outperforms baselines in both simulation and real‑robot experiments, especially when temporal patterns are learnable.
By Mingjian Gao, Zhaocheng Li, Haoyang Huang, Wenqiao Zhang, Yingjie Niu, Hao Zhou, Chao Li, Juncheng Li, Siliang Tang, Yueting Zhuang
CST‑WM is a causally structured world model designed for embodied visual tracking, where a robot must keep a moving target visible and recover it after occlusion or drift. The model separates state into target‑evidence, robot, and observation branches, removing direct action‑to‑target‑evidence edges to prevent causal hallucination and instead letting actions influence evidence through robot motion and resulting views. Evaluated on EVT‑Bench, Habitat 3.0, and real‑world trials with a Unitree Go2 quadruped, CST‑WM outperforms reactive trackers and other world‑model baselines in following, distance control, safety, and re‑acquisition, achieving 20 of 30 successful real‑world recoveries versus 14 for TrackVLA.
By Junyi Hu, Shuaihang Yuan, Jiazhao Liang, Yi Fang
arXiv:2609.18363v1 Announce Type: new
Abstract: Online multi camera 3D tracking must maintain scene global identities across synchronized views, yet query-based trackers carry these identities only i...
By Pragyan Shrestha, Haruto Nakayama, Atom Scott
LPA-CWM introduces a Learned Physical Adjudicator (LPA) to improve counterfactual world models (CWM) for motion reasoning by learning to weight candidate responses based on visual context and response structure. The 3.0M‑parameter LPA is trained on dense MOVi‑F trajectories while keeping the CWM predictor and intervention generator frozen. A new Completeness‑aware Motion Correspondence (CMC) protocol evaluates localization, trajectory completeness, visibility, and continuity, and LPA‑CWM achieves significant gains on DAVIS and Kinetics subsets.
By Kunwei Wu, Xiang Liu, Guocai Yao, Junming Chen, Zhikang Chen, Min Zhang, Pengwei Wang, Sen Cui
VideoReloc presents a method for long‑term indoor video relocalization that relies on a compact semantic scene graph rather than visual appearance. By adaptively selecting clip lengths based on odometry and object‑motion criteria, the system gathers spatial evidence, verifies poses through object triplets, and refines orientation using box faces and gravity cues. This approach achieves high localization accuracy with a tiny 100 kB map, outperforming traditional appearance‑based methods on RIO10 and ReplicaCAD datasets.
By Qianru Li, Xuyang Chen, Xuqin Wang, Zhenghao Zhang, Hongyi Luo, Tao Wu, Daniel Cremers, Lu Liu, Yanfeng Zhang
arXiv:2609.39832v1 Announce Type: new
Abstract: Many visual tracking methods use rejection mechanisms to suppress unreliable predictions. However, these mechanisms can also reject correctly localized...
By Youbin He, Siwei Wang