arXiv AI

GROW$^2$: Grounding Which and Where for Robot Tool Use

arXiv:2606. 30632v1 Announce Type: cross Abstract: Can the robot use a plate to cut a cake if no knife is available?

arXiv AI

Jul 8

Harnessing Generative Image Models for Training-Free Primitive Shape Abstraction

arXiv:2607. 05568v1 Announce Type: cross Abstract: Representing 3D shapes as compact sets of geometric primitives is fundamental to robotics, simulation, and scene understanding.

By Gregor Kobsik, Tim Elsner, Leif Kobbelt

llms computer-vision robotics fine-tuning multimodal

arXiv AI

Jul 8

OpenGround: Planning-based Online Perception for Open-World 3D Visual Grounding

arXiv:2512. 23020v3 Announce Type: replace-cross Abstract: 3D visual grounding aims to locate objects based on natural language descriptions in 3D scenes.

By Wenyuan Huang, Zhenyu Zhang, Zhao Wang, Zhou Wei, Ting Huang, Fang Zhao, Jian Yang

llms benchmarks

arXiv AI

Jun 2

AffordGen: Generating Diverse Demonstrations for Generalizable Object Manipulation with Afford Correspondence

arXiv:2604. 10579v2 Announce Type: replace-cross Abstract: Despite the recent success of modern imitation learning methods in robot manipulation, their performance is often constrained by geometric variations due to limited data diversity.

By Jiawei Zhang, Kaizhe Hu, Yingqian Huang, Yuanchen Ju, Zhengrong Xue, Huazhe Xu

robotics

arXiv AI

Jun 24

G$^3$VLA: Geometric inductive bias for Vision-Language-Action Models

arXiv:2606. 24472v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have made rapid progress in generalist robot manipulation by harnessing semantic knowledge from pretrained vision-language backbones, but their visual tokens remain grounded in 2D image coordinates rather than the calibrated geometry of the robot's cameras -- a mismatch especially pronounced in multi-camera setups, where views are coupled by known intrinsics and extrinsics yet processed as independent images.

By Yue Peng, Yongzhe Zhao, Artur Habuda, Khuyen Pham, Yanheng Zhu, Tran Nguyen Le, Fares Abu-Dakka, Li Guo

rag robotics multimodal safety

arXiv AI

Jun 30

RoboGaze: Evaluating Robot World Models via Structured Vision-Language Analysis

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

llms agents diffusion robotics multimodal benchmarks safety

arXiv AI

2d ago

Evolve Vision-Language-Action Model into an Agent with On-the-fly Tool-use

arXiv:2608. 14047v1 Announce Type: cross Abstract: This paper integrates end-to-end Visual-Language-Action (VLA) models with agentic tool-use to propose Agentic Robot with Tool-use (ART).

By Yi Ding, Yanzhao Yu, Xili Dai, Xianbiao Qi, Peiwen Sun, Xueqian Wang, Xiangyu Yue, Jianan Wang

agents robotics multimodal

arXiv AI

6d ago

G0.5: One Autoregressive Stream for Robot Reasoning and Action

arXiv:2608. 11739v1 Announce Type: cross Abstract: The prevailing recipe for Vision-Language-Action (VLA) models couples a pretrained VLM with a separately trained flow-matching action expert.

By Yicheng Liu, Zibin Dong, Baijun Ye, Tianyuan Yuan, Tao Jiang, Anqi Yang, Shicheng Cao, Haonan Liu, Yue Sun, Zihan Guo, Xiao Liu, Dong Ke, Changxun Pan, Chenru Wu, Tailai Cheng, Xiaoshu Ren, Xinlei Zhang, Jianning Cui, Zijie Zhao, Haoyu Zhang, Kaiming Xu, Haodong Yang, Bowen Zhang, Jiahui Niu, Shaoting Zhu, Shiduo Zhang, Hang Zhao

llms nlp robotics fine-tuning multimodal benchmarks

arXiv AI

Jul 7

Token-Based Affordance Grounding with Large Vision-Language Models

arXiv:2607. 03595v1 Announce Type: cross Abstract: Affordance grounding aims to localize image regions that support a specific action, serving as a core capability for physical intelligence and embodied perception.

By Seung Il Lee, Qinqian Lei, Daguang Xu, Dong Yang, Robby T. Tan, Yixin Chen, Bo Wang

llms robotics multimodal benchmarks

arXiv AI

Jun 2

Bridging the 2D-3D Gap: A Hierarchical Semantic-Geometric Map for Vision Language Navigation

arXiv:2606. 00095v1 Announce Type: cross Abstract: Vision-Language Navigation (VLN) enables embodied agents to reach target locations in unseen environments by following language instructions.

By Kailing Li, Tianwen Qian, Lijin Yang, Yuqian Fu, Jingyu Gong, Xiaoling Wang, Liang He

llms agents robotics multimodal benchmarks

Hugging Face Trending Papers

Jul 5

VLA Grounder: Language-Conditioning Space Optimization for Black-Box VLA Models

Vision-Language-Action (VLA) models are commonly treated as end-to-end action policies conditioned on natural-language task descriptions. In practice, however, their behavior often depends sharply on how the instruction is phrased, suggesting that language is not merely a task label but an optimizable conditioning input.

reinforcement-learning robotics multimodal

arXiv AI

Jun 9

ACTIVE-o3: Empowering MLLMs with Active Perception via Pure Reinforcement Learning

arXiv:2505. 21457v2 Announce Type: replace-cross Abstract: Active vision, also known as active perception, refers to actively selecting where and how to look in order to gather task-relevant information.

By Muzhi Zhu, Hao Zhong, Canyu Zhao, Zongze Du, Mingyu Liu, Zheng Huang, Anzhou Li, Hao Chen, Cheng Zou, Jingdong Chen, Ming Yang, Chunhua Shen

llms agents computer-vision reinforcement-learning robotics multimodal benchmarks

arXiv AI

Jun 12

Bounding Boxes as Goals: Language-Conditioned Grasping via Neuro-Symbolic Planning

arXiv:2606. 12910v1 Announce Type: cross Abstract: For robotics to be effectively integrated into household or industrial environments, machines must adapt to natural-language prompts in real time.

By Allison Andreyev, Landon Eum, Nestor Tiglao, Romel Gomez

llms robotics fine-tuning multimodal benchmarks

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