1 citations · 2 across the 18 of their papers we have counts for
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NavHarness: Adaptive Goals for Agentic Vision-Language Navigation
Haoxiang Shi, Zaijing Li, Muhe Ding +3
Vision-Language Navigation (VLN) requires embodied agents to generate actions based on instructions and observations. General-purpose multimodal agents offer a promising basis for…
Technical Report of RoboSpatial Challenge at CVPR 2026: Selective Reasoning Activation and Reference-Frame Disambiguation for Embodied Spatial Reasoning
Yuxiang Xie, Qi Lv, Jianming Xing +4
Vision-language models achieve strong general perception but often struggle with the spatial reasoning required for embodied tasks. We present RoboSpatialBrain, our submission to t…
Efficient Visual Pointing for Embodied AI:Agent-Driven Data Synthesis, Cross-Block Attention, and Iterative Correction
Zijian Hong, Qi Lv, Yuxiang Xie +4
Visual pointing maps a language instruction to pixel co ordinates, a core skill for embodied AI. We describe our PointArena 2026 solution, which achieves 77.2% overall accuracy and…
Task-Aware 3D Affordance Segmentation via 2D Guidance and Geometric Refinement
Lian He, Meng Liu, Qilang Ye +3
Understanding 3D scene-level affordances from natural language instructions is essential for enabling embodied agents to interact meaningfully in complex environments. However, thi…
3D-AffordanceLLM: Harnessing Large Language Models for Open-Vocabulary Affordance Detection in 3D Worlds
Hengshuo Chu, Xiang Deng, Qi Lv +4
3D Affordance detection is a challenging problem with broad applications on various robotic tasks. Existing methods typically formulate the detection paradigm as a label-based sema…
Embodied Crowd Counting
Runling Long, Yunlong Wang, Jia Wan +5
Occlusion is one of the fundamental challenges in crowd counting. In the community, various data-driven approaches have been developed to address this issue, yet their effectivenes…
LION : Empowering Multimodal Large Language Model with Dual-Level Visual Knowledge
Gongwei Chen, Leyang Shen, Rui Shao +2
Multimodal Large Language Models (MLLMs) have endowed LLMs with the ability to perceive and understand multi-modal signals. However, most of the existing MLLMs mainly adopt vision…