arXiv:2609.25627v1 Announce Type: cross
Abstract: General-purpose robot control requires models to understand task intent, identify where to interact, capture how the scene evolves, and generate prec...
By Haoran Wen, Wenfu Wang, Kunsong Shi, Jingke Wang, Wancheng Feng, Yiren Zhang, Yueran Zhao, Xuancheng Zhang, Nanfei Ye, Xingru Chen, Zhaohong Sun, Chengmin Yang, Zikang Yu, Penghao Bi, Jia Shi, Yu Liu, Kun Zhan, Yan Xie
arXiv:2610.34051v1 Announce Type: cross
Abstract: Robot tasks require grounding spatial language in physical scenes, but task success alone cannot establish whether spatial instructions are followed....
By Sirui Chen, Huang Huang, Qineng Wang, Wenlong Huang, Manling Li, Ruohan Zhang, Li Fei-Fei, Jiajun Wu
World Action Agent (WAA) is a multi‑agent framework that lets vision‑language models (VLMs) directly pilot robots by operating within a visual action workspace. The workspace provides automatically selected contact views, editable action rehearsals, and in‑view correction to refine decisions before low‑level execution. WAA learns procedural skills from expert videos and human teaching, and its interaction traces can train smaller VLMs, achieving state‑of‑the‑art success on LIBERO‑Pro and improving out‑of‑domain performance on robosuite and Qwen3.5‑9B.
By Yehang Zhang, Haojian Huang, Yifan Chang, Jianchong Su, Bohan Zhou, Yingjie Xu, Wosong Chen, Tianhao Zhou, Chenxu Wang, Tianyi Zhang, Yangkai Wei, Wenqian Li, Shiyuan Deng, Yinchuan Li, Ying-Cong Chen, Zexi Li
CLOSER is an evidence‑aware active visual verification framework for embodied object‑goal navigation. It fine‑tunes a vision‑language model to label each view of a tracked candidate as target, non‑target, or uncertain, then selects complementary views using an embedding‑based diversity gate and fuses their probabilities with a lightweight network. When uncertainty remains, a diffusion policy directs the agent toward the candidate using only the tracked mask and depth until a decisive judgment is reached.
By Pengying Wu, Mingye Jiang, Yunze Hu, Zhejia Wen, Kangjie Zhou, Zongru Yang, Zhe Peng, Chang Liu
arXiv:2610.34149v1 Announce Type: cross
Abstract: While Multimodal Large Language Models (MLLMs) excel at zero-shot reasoning, their spatial capabilities are primarily evaluated through static, passi...
By Tianchen Guo, Tengyue Zhang, Chen Liu, Ling Chen, Xin Yu
SpaceAct is a new benchmark that tests multimodal large language models on 3D spatial and temporal reasoning through closed‑loop physical action and active perception. It uses interlocking bricks and mechanical puzzles to create 108 verified instances across 36 task families, requiring models to reconstruct 3D geometry, anticipate dynamics, and execute precise 6‑DoF poses while avoiding collisions. The accompanying SAPA framework provides diagnostic modes and active‑perception capabilities, revealing that even with a valid plan, current models struggle with 3D visual grounding and action refinement.
By Chenglong Shi, Zekai Yuan, Yue Song, Jon Pu, Will Wu