arXiv:2609.27076v1 Announce Type: new
Abstract: Open-vocabulary visual grounding enables robots to localise task-relevant entities from natural-language queries without dependence on predefined perce...
By Linus Nwankwo, Muslim Alaran, Christian Rauch, Stanley Chukwuebuka Obilikpa, Elmar Rueckert
arXiv:2609.08402v1 Announce Type: cross
Abstract: Air-Ground Object Search (AGOS) in urban environments is a challenging embodied task, which requires an Unmanned Aerial Vehicle (UAV) and an Unmanned...
By Boao Yu, Zimo Chen, Junreng Rao, Yue Hu, Zhengqiu Zhu, Yong Zhao, Rusheng Ju
arXiv:2608. 11738v1 Announce Type: cross Abstract: Multimodal Large Language Model (MLLM)-based UAV aerial image understanding and reasoning is essential for aerial intelligence yet poses distinct challenges arising from extreme scale variation, arbitrary camera orientations, and high object density.
By Haoyu Zhang, Shuoxun Zhang, Peng Ye, Lin Zhang, Jiakang Yuan, Shenghong Yi, Yuening Wang, Tao Chen
AMIGO (Agentic Multi-Image Grounding Oracle Benchmark) is a long-horizon evaluation framework for vision‑language models that tests hidden‑target identification across galleries of visually similar images. The benchmark requires a model to ask a sequence of attribute‑focused Yes/No questions, receiving Yes/No/Unsure feedback and penalizing invalid actions with Skip, thereby stressing question selection under uncertainty, constraint tracking, and fine‑grained discrimination. Using the Guess My Preferred Dress task, the study shows that final‑answer accuracy alone overstates performance, as models may guess correctly without verified evidence, waste turns, or violate the protocol, highlighting the need for combined visual discrimination, informative questioning, and robust protocol adherence.
By Min Wang, Ata Mahjoubfar
arXiv:2606. 28385v1 Announce Type: cross Abstract: Recent advances in robot world models enable synthetic video generation for embodied prediction and planning.
By Minh-Loi Nguyen, Nghiem Tuong Diep, Hung Khang Nguyen, Minh Le, Doanh Le Thien, Hoang H. Tran, Dung D. Le, Vu N. Duong, Daniel Sonntag, An Thai Le, Duy Minh Ho Nguyen, Vien Anh Ngo, Tran Van Nhiem
arXiv:2608. 07557v1 Announce Type: cross Abstract: Vision-Language Navigation for Unmanned Aerial Vehicles (UAV-VLN) requires rapid and reactive control in complex 3D environments.
By Peng Xu, Chengcheng Wang, Shaohua Wan