arXiv AI

Co-GLANCE: Uncertainty-Aware Active Perception for Heterogeneous Robot Teaming

arXiv:2606. 09919v1 Announce Type: cross Abstract: Perceptual uncertainty is a central challenge for heterogeneous robot teams operating in unstructured outdoor environments, where no single viewpoint affords reliable scene understanding.

arXiv Computer Vision

6d ago

CoVLM-Bench: A Real-World Benchmark for Cooperative Driving Question Answering and Planning

arXiv:2609.35823v1 Announce Type: new Abstract: Vision-language models (VLMs) have made substantial progress in autonomous driving, but their success has primarily been studied in ego-centric scenes....

By Kang Yang, Shuai Liu, Hang Li, Yance Fang, Deying Li, Yongcai Wang

llms agents nlp robotics multimodal benchmarks

arXiv Computer Vision

Sep 23

VEOcc: Voxel-Centric Online Semantic Occupancy Prediction For Embodied Scene Understanding

arXiv:2605.25059v4 Announce Type: replace Abstract: Crucial for autonomous exploration, online 3D occupancy prediction and mapping incrementally construct dense spatial representations on the fly. Em...

By Ruoyu Wang, Yong Liu, Jiahan Li, Sheng Tao, Yuhang Lin, Yukai Ma

agents robotics benchmarks

arXiv AI

Aug 11

AeroDPO: Unleashing Lightweight UAV Navigation with High-Fidelity Perception and Automated Preference Optimization

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

llms agents efficiency multimodal benchmarks

arXiv AI

Sep 23

Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence

arXiv:2510.26915v2 Announce Type: replace-cross Abstract: While heterogeneous teams have typically been designed for well-specified missions with known semantics, generative intelligence, i.e., large...

By Zachary Ravichandran, Fernando Cladera, Ankit Prabhu, Jason Hughes, Carlos Nieto-Granda, Varun Murali, Camillo Taylor, George J. Pappas, Vijay Kumar

llms robotics multimodal

arXiv AI

Jun 29

SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion

arXiv:2606. 27876v1 Announce Type: cross Abstract: Spatial intelligence is essential for low-altitude unmanned aerial vehicle (UAV) perception, collaboration, and navigation.

By Haoyu Zhang, Meng Liu, Qianlong Xiang, Kun Wang, Yaowei Wang, Liqiang Nie

llms multimodal benchmarks

Hugging Face Trending Papers

Jun 16

Uncertainty Quantification for Flow-Based Vision-Language-Action Models

Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets. Despite their strong empirical performance in robotic manipulation, VLAs lack mechanisms to quantify confidence in their predictions and to detect when their actions may be unreliable.

diffusion robotics fine-tuning multimodal benchmarks

arXiv Computer Vision

Sep 21

VeriFuse: Bounded Vision-Language Arbitration and Reason-Guided Refinement for Cooperative 3D Perception

VeriFuse is a bounded arbitration framework that integrates vision‑language models (VLMs) into vehicle‑infrastructure cooperative 3D perception. Each agent first generates independent detections, then VeriFuse creates a unified candidate pool of geometric proposals and cross‑source hypotheses. A frozen VLM selects among three actions—SELECT, REFINE, or REJECT—to resolve ambiguity and produce final 3D detections, achieving strong AP50/AP70 scores on the DAIR‑V2X dataset while keeping vehicle‑side BEV AP50 drop minimal under delay.

By Hongyi Lin, Yiyao Liu, Qi Kang, Heye Huang, Yang Liu, Haris Koutsopoulos, Jinhua Zhao

llms agents multimodal

arXiv AI

Jul 2

What's Hidden Matters: Identifying Planning-Critical Occluded Agents using Vision-Language Models

arXiv:2607. 00283v1 Announce Type: cross Abstract: Autonomous vehicles must safely navigate complex environments where planning-critical agents may be hidden from view.

By Amirhosein Chahe, Tyler Naes, Jovin D'sa, Faizan M. Tariq, Sangjae Bae, Lifeng Zhou, David Isele

llms agents robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 17

Uncertainty Quantification for Flow-Based Vision-Language-Action Models

arXiv:2606. 18043v1 Announce Type: cross Abstract: Vision-language-action models (VLAs) combine vision-language backbones with expressive generative action heads trained via flow matching on large-scale robotic datasets.

By Ralf R\"omer, Maximilian Seeliger, Saida Liu, Ben Sturgis, Marco Bagatella, Daniel Marta, Andreas Krause, Angela P. Schoellig

diffusion robotics fine-tuning multimodal benchmarks

arXiv AI

Aug 6

Explicit Language Memory for Long-Horizon Planning in Vision-Language-Action Models

arXiv:2608. 04765v1 Announce Type: cross Abstract: Vision-language-action (VLA) models provide a unified paradigm for connecting visual perception, language understanding, and robotic control.

By Houze Xu, Jizhong Li, Ziyi Ye

llms nlp robotics fine-tuning multimodal

arXiv AI

Jul 24

VoLN: Vision-Only Long-Horizon Navigation---Paradigm, Benchmark, and Method

arXiv:2607. 21400v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) enables embodied agents to follow natural-language instructions.

By Jiabin Lou, Haopeng Wang, Yuanshuai Wang, Xinyu Liu, Xuxin Lv, Yuxin Guo, Lei Huang, Rongye Shi, Wenjun Wu

agents robotics benchmarks

arXiv AI

Jun 30

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?

By Yuhong Deng, Yuyao Liu, David Hsu

llms agents robotics multimodal benchmarks

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