Pointing-based visual grounding requires models to precisely locate target objects by deciphering complex spatial relationships between the visual scene and pointing gestures. Traditional methods typically encode input images into static feature representations and perform reasoning primarily within the linguistic domain, often overlooking the rich perceptual cues and explicit spatial geometry inherent in images.
arXiv:2606. 15753v1 Announce Type: new Abstract: Embodied reasoning requires models to perceive task-relevant objects and spaces in physical environments and maintain consistent visual grounding throughout multi-step reasoning.
By Yaoting Huang, Yifu Yuan, Linqi Han, Chengwen Li, Shuoheng Zhang, Xianze Yao, Hongyao Tang, Yan Zheng, Jianye Hao
InsightMap is a framework that uses top‑down maps as explicit spatial memory and action‑conditioned prediction targets for language‑guided navigation. It links historical views to labeled map locations and employs a shared multimodal backbone to jointly learn navigation action prediction and post‑action map generation, providing auxiliary training supervision. The approach supports a unified RGB‑D pipeline for navigation, visual question answering, situated reasoning, and 3D grounding, achieving state‑of‑the‑art results on R2R‑CE, RxR‑CE, ScanQA, SQA3D, ScanRefer, and outperforming baselines on the Unitree Go2 platform.
By Hongpei Zheng, Hujun Yin
The paper introduces GTA‑VLA, an interactive Vision‑Language‑Action framework that lets users guide robot policies with explicit visual cues such as affordance points, boxes, and traces. Unlike traditional direct sense‑to‑act models, GTA‑VLA incorporates a spatial‑visual Chain‑of‑Thought that blends human guidance with internal task planning, and couples this reasoning module with a lightweight reactive action head for efficient execution. Experiments on the SimplerEnv WidowX benchmark show a state‑of‑the‑art 81.2 % success rate, and the framework significantly improves task success under out‑of‑domain visual shifts and spatial ambiguities, demonstrating the benefit of interactive reasoning for failure recovery in embodied control.
By Yiran Ling, Qing Lian, Jinghang Li, Qing Jiang, Tianming Zhang, Xiaoke Jiang, Chuanxiu Liu, Jie Liu, Lei Zhang
arXiv:2604. 08991v3 Announce Type: replace-cross Abstract: Reliable embodied interaction in indoor environments requires agents to precisely localize small everyday objects from visual observations.
By Zhiyu Zhou, Peilin Liu, Ruoxuan Zhang, Luyang Zhang, Cheng Zhang, Hongxia Xie, Wen-Huang Cheng
arXiv:2605.03927v3 Announce Type: replace
Abstract: Vision-language models have demonstrated strong performance across robotic perception and instruction-following tasks. However, they still struggle...
By Xiaowen Sun, Matthias Kerzel, Mengdi Li, Xufeng Zhao, Paul Striker, Stefan Wermter
Anvil is a unified framework for embodied spatial intelligence that integrates data generation, training, and benchmarking. It introduces three components: Anvil-Gen, a heterogeneous data flywheel that generates and verifies cross‑stage consistency; Anvil-Ben, which provides scene‑aligned supervision and assessments to separate stage competence from error propagation; and Anvil‑MOPD, a 9B student model that distills spatial knowledge from specialist teachers using semantic‑first fault routing and stage normalization. The system builds 5.8 M source‑grounded records, trains on 5.22 M, and evaluates on 580 K, outperforming state‑of‑the‑art models on seven public benchmarks.
By Weilin Ruan, Bohan Zhou, Jinghao Li, Yangkai Wei, Wenqian Li, Peiran Dong, Yingjie Xu, Leyi Wu, Haojian Huang, Binghui Xie, Han Yang, Kaiwen Zhou, Yinchuan Li, Ying-Cong Chen, James Cheng
Spatial-OPSD is a label‑free self‑improvement framework for vision‑language models that leverages spatial priors such as depth, 3D relations, and camera geometry to provide dense token‑level supervision. During training, a privileged teacher uses these priors while the student learns from only the original visual‑language input, and a recursive round‑wise scheme allows repeated self‑improvement without moving the teacher. Across four VLM families, one round of Spatial‑OPSD improves the five‑benchmark average, and three rounds push a strong spatially specialized model to the open‑source frontier, achieving the highest average among open models and best results on three of five spatial reasoning benchmarks.
By Zhenyu Liu, Zhangquan Chen, Keyi Chen, Mingze Sun, Xiang An, Haodong Jing, Ruqi Huang
arXiv:2606. 16122v1 Announce Type: new Abstract: Visual thinking should not only sound right; it should show its evidence.
By Junkai Zhang, Yihe Deng, Kai-Wei Chang, Wei Wang
PointRL introduces a verifiable reinforcement learning framework that learns point-level vision‑language grounding from heterogeneous annotation evidence such as bounding boxes, masks, and instance labels. The method converts these annotations into pointing instructions while preserving target supports, instance membership, and set constraints as hidden verifier evidence, which a deterministic checker uses to score predictions. Evaluation on PointArena shows that PointRL improves Qwen3.5‑4B’s accuracy from 56.11% to 65.58%, and similar gains are observed on RoboSpatial, BLINK, and Ref‑Adv benchmarks.
By Jingyang Su, Pu Cao, Xiuze Jin, Longyue Zhang, Qing Song, Lu Yang
arXiv:2607. 16322v1 Announce Type: cross Abstract: Micro-gesture recognition demands the detection of fleeting, spatially localized movements that are frequently overwhelmed by dominant static appearances and background noise.
By Taorui Wang, Wei Xia, Hui Ma, Zijia Song, Jiayu Zhang, Zeheng Wang, Yong Xu, Zitong Yu
GeoLatent introduces a geometry‑guided latent structuring approach for 3D reasoning from 2D images, separating position, direction, and global geometry under geometric supervision. It combines Common–Residual Geometry Alignment (CR‑GEO) with routed optimization to prevent latent collapse and to direct visual answer learning through the latents while maintaining full attention. The method achieves state‑of‑the‑art performance on SPAR‑Bench and SPBench, improving geometry effective rank and overall accuracy.
By Yakun Zhu, Yi Bin, Yujuan Ding, Zheng Wang, Pengpeng Zeng, Duo Peng, Jingkuan Song, Heng Tao Shen