id string | sources list | title string | abstract string | authors list | categories list | fields_of_study list | published_date timestamp[s] | url string | pdf_url string | arxiv_id string | doi string | citation_count int64 | influential_citation_count int64 | has_code bool | code_url string | venue string | quality_score float64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
c9f1aec7c128b4676e5e11c025989acfd1e6a6e53c1c5bb89bbf507051ad0b19 | [
"arxiv",
"semantic_scholar"
] | EdgeVLA: Efficient Vision-Language-Action Models | Vision-Language Models (VLMs) have emerged as a promising approach to address the data scarcity challenge in robotics, enabling the development of generalizable visuomotor control policies. While models like OpenVLA showcase the potential of this paradigm, deploying large-scale VLMs on resource-constrained mobile manip... | [
"Paweł Budzianowski",
"Wesley Maa",
"Matthew Freed",
"Jingxiang Mo",
"Winston Hsiao",
"Aaron Xie",
"Tomasz Młoduchowski",
"Viraj Tipnis",
"Benjamin Bolte"
] | [
"cs.RO",
"cs.CL"
] | [
"Computer Science"
] | 2025-07-18T00:00:00 | https://arxiv.org/abs/2507.14049 | https://arxiv.org/pdf/2507.14049v1 | 2507.14049 | 10.48550/arXiv.2507.14049 | 19 | 0 | true | https://github.com/kscalelabs/evla | arXiv.org | 0.3612 |
23dbbe82d75e6e8f8b27270dac1e36459f252a69ceece9faf5ab6dde825c61fb | [
"arxiv",
"semantic_scholar"
] | Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities | Recent Vision-and-Language Navigation (VLN) advancements are promising, but their idealized assumptions about robot movement and control fail to reflect physically embodied deployment challenges. To bridge this gap, we introduce VLN-PE, a physically realistic VLN platform supporting humanoid, quadruped, and wheeled rob... | [
"Liuyi Wang",
"Xinyuan Xia",
"Hui Zhao",
"Hanqing Wang",
"Tai Wang",
"Yilun Chen",
"Chengju Liu",
"Qijun Chen",
"Jiangmiao Pang"
] | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2025-07-17T00:00:00 | https://arxiv.org/abs/2507.13019 | https://arxiv.org/pdf/2507.13019v2 | 2507.13019 | 10.1109/ICCV51701.2025.00882 | 23 | 1 | true | null | IEEE International Conference on Computer Vision | 0.3595 |
4129883729aa9027bcd8aa2510e8a1090afb13180afbcc96b8cdf4246dc4b92f | [
"arxiv",
"semantic_scholar"
] | The Developments and Challenges towards Dexterous and Embodied Robotic Manipulation: A Survey | Achieving human-like dexterous robotic manipulation remains a central goal and a pivotal challenge in robotics. The development of Artificial Intelligence (AI) has allowed rapid progress in robotic manipulation. This survey summarizes the evolution of robotic manipulation from mechanical programming to embodied intelli... | [
"Gaofeng Li",
"Ruize Wang",
"Peisen Xu",
"Qi Ye",
"Jiming Chen"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-07-16T00:00:00 | https://arxiv.org/abs/2507.11840 | https://arxiv.org/pdf/2507.11840v2 | 2507.11840 | 10.48550/arXiv.2507.11840 | 11 | 0 | false | null | arXiv.org | 0.2698 |
ec74f7e296743742128bb2bc5b45c7e8d6ccb9b1905b81c98906b8e7435752ca | [
"arxiv",
"semantic_scholar"
] | LLM-based ambiguity detection in natural language instructions for collaborative surgical robots | Ambiguity in natural language instructions poses significant risks in safety-critical human-robot interaction, particularly in domains such as surgery. To address this, we propose a framework that uses Large Language Models (LLMs) for ambiguity detection specifically designed for collaborative surgical scenarios. Our m... | [
"Ana Davila",
"Jacinto Colan",
"Yasuhisa Hasegawa"
] | [
"cs.RO",
"cs.HC"
] | [
"Computer Science"
] | 2025-07-15T00:00:00 | https://arxiv.org/abs/2507.11525 | https://arxiv.org/pdf/2507.11525v1 | 2507.11525 | 10.1109/RO-MAN63969.2025.11217610 | 5 | 0 | false | null | IEEE International Symposium on Robot and Human Interactive Communication | 0.2303 |
96e2e80dcf3a5a12a00a5482083d9eef112c9ab707d794c60775580b75db5755 | [
"arxiv",
"semantic_scholar"
] | Human-Robot collaboration in surgery: Advances and challenges towards autonomous surgical assistants | Human-robot collaboration in surgery represents a significant area of research, driven by the increasing capability of autonomous robotic systems to assist surgeons in complex procedures. This systematic review examines the advancements and persistent challenges in the development of autonomous surgical robotic assista... | [
"Jacinto Colan",
"Ana Davila",
"Yutaro Yamada",
"Yasuhisa Hasegawa"
] | [
"cs.RO",
"cs.HC"
] | [
"Computer Science"
] | 2025-07-15T00:00:00 | https://arxiv.org/abs/2507.11460 | https://arxiv.org/pdf/2507.11460v1 | 2507.11460 | 10.1109/RO-MAN63969.2025.11217781 | 4 | 0 | false | null | IEEE International Symposium on Robot and Human Interactive Communication | 0.2303 |
96dac375f42ae524684aae5ea102ef78afddd9510ddefa39310af8d42175b7d2 | [
"arxiv",
"semantic_scholar"
] | Vision Language Action Models in Robotic Manipulation: A Systematic Review | Vision Language Action (VLA) models represent a transformative shift in robotics, with the aim of unifying visual perception, natural language understanding, and embodied control within a single learning framework. This review presents a comprehensive and forward-looking synthesis of the VLA paradigm, with a particular... | [
"Muhayy Ud Din",
"Waseem Akram",
"Lyes Saad Saoud",
"Jan Rosell",
"Irfan Hussain"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-07-14T00:00:00 | https://arxiv.org/abs/2507.10672 | https://arxiv.org/pdf/2507.10672v1 | 2507.10672 | 10.48550/arXiv.2507.10672 | 39 | 1 | false | null | arXiv.org | 0.4005 |
2915aaf288bfa80ce07dd1a988e4bb540bf47d7b29c18a6b350f752230e8e338 | [
"arxiv"
] | Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization | Vision-Language-Action (VLA) models have shown remarkable achievements, driven by the rich implicit knowledge of their vision-language components. However, achieving generalist robotic agents demands precise grounding into physical interactions, especially in contact-rich scenarios where fine-grained force control is e... | [
"Jialei Huang",
"Shuo Wang",
"Fanqi Lin",
"Yihang Hu",
"Chuan Wen",
"Yang Gao"
] | [
"cs.RO",
"cs.LG"
] | [] | 2025-07-12T00:00:00 | https://arxiv.org/abs/2507.09160 | https://arxiv.org/pdf/2507.09160v1 | 2507.09160 | null | 0 | 0 | false | null | null | 0.1444 |
a0a4ff3ea134c5d0ef3b2b4f20ce7229e0ea2729dd04698ee7295881d524fee4 | [
"arxiv",
"semantic_scholar"
] | Vision-Language-Vision Auto-Encoder: Scalable Knowledge Distillation from Diffusion Models | Building state-of-the-art Vision-Language Models (VLMs) with strong captioning capabilities typically necessitates training on billions of high-quality image-text pairs, requiring millions of GPU hours. This paper introduces the Vision-Language-Vision (VLV) auto-encoder framework, which strategically leverages key pret... | [
"Tiezheng Zhang",
"Yitong Li",
"Yu-cheng Chou",
"Jieneng Chen",
"Alan Yuille",
"Chen Wei",
"Junfei Xiao"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-07-09T00:00:00 | https://arxiv.org/abs/2507.07104 | https://arxiv.org/pdf/2507.07104v2 | 2507.07104 | 10.48550/arXiv.2507.07104 | 2 | 0 | false | null | arXiv.org | 0.2234 |
7f68ae933e4e173f9b728b02dfbcfe10dfe4aa373c4604e7ae37553deb62ec07 | [
"arxiv",
"semantic_scholar"
] | EC-Flow: Enabling Versatile Robotic Manipulation from Action-Unlabeled Videos via Embodiment-Centric Flow | Current language-guided robotic manipulation systems often require low-level action-labeled datasets for imitation learning. While object-centric flow prediction methods mitigate this issue, they remain limited to scenarios involving rigid objects with clear displacement and minimal occlusion. In this work, we present ... | [
"Yixiang Chen",
"Peiyan Li",
"Yan Huang",
"Jiabing Yang",
"Kehan Chen",
"Liang Wang"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2025-07-08T00:00:00 | https://arxiv.org/abs/2507.06224 | https://arxiv.org/pdf/2507.06224v1 | 2507.06224 | 10.1109/ICCV51701.2025.01112 | 6 | 0 | false | null | IEEE International Conference on Computer Vision | 0.2223 |
6216dbd38279cfb70c2f4ce55f99e95f514db3d4a5fe5377ae0ba0445003425d | [
"arxiv",
"semantic_scholar"
] | VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting | Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural language. However, current VLA models suffer from two drawbacks: (i) generation of massive tokens leading to high inference latency and increased training cost, and (ii) insufficient ut... | [
"Juyi Lin",
"Amir Taherin",
"Arash Akbari",
"Arman Akbari",
"Lei Lu",
"Guangyu Chen",
"Taskin Padir",
"Xiaomeng Yang",
"Weiwei Chen",
"Yiqian Li",
"Xue Lin",
"David Kaeli",
"Pu Zhao",
"Yanzhi Wang"
] | [
"cs.CV",
"cs.AI",
"cs.RO"
] | [
"Computer Science"
] | 2025-07-07T00:00:00 | https://arxiv.org/abs/2507.05116 | https://arxiv.org/pdf/2507.05116v4 | 2507.05116 | 10.48550/arXiv.2507.05116 | 18 | 0 | true | https://github.com/LukeLIN-web/VOTE | arXiv.org | 0.3418 |
ebb0b3429dd98df8b23117559526bbc4e3a94425249b0de620da68ee9fcebb40 | [
"arxiv",
"semantic_scholar"
] | Move to Understand a 3D Scene: Bridging Visual Grounding and Exploration for Efficient and Versatile Embodied Navigation | Embodied scene understanding requires not only comprehending visual-spatial information that has been observed but also determining where to explore next in the 3D physical world. Existing 3D Vision-Language (3D-VL) models primarily focus on grounding objects in static observations from 3D reconstruction, such as meshe... | [
"Ziyu Zhu",
"Xilin Wang",
"Yixuan Li",
"Zhuofan Zhang",
"Xiaojian Ma",
"Yixin Chen",
"Baoxiong Jia",
"Wei Liang",
"Qian Yu",
"Zhidong Deng",
"Siyuan Huang",
"Qing Li"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-07-05T00:00:00 | https://arxiv.org/abs/2507.04047 | https://arxiv.org/pdf/2507.04047v2 | 2507.04047 | 10.1109/ICCV51701.2025.00761 | 58 | 8 | false | null | IEEE International Conference on Computer Vision | 0.4771 |
512200ecb22c6ae6ca0ab771d3699942194de20c2d176e4c914df94ff4e54186 | [
"arxiv",
"semantic_scholar"
] | cVLA: Towards Efficient Camera-Space VLAs | Vision-Language-Action (VLA) models offer a compelling framework for tackling complex robotic manipulation tasks, but they are often expensive to train. In this paper, we propose a novel VLA approach that leverages the competitive performance of Vision Language Models (VLMs) on 2D images to directly infer robot end-eff... | [
"Max Argus",
"Jelena Bratulic",
"Houman Masnavi",
"Maxim Velikanov",
"Nick Heppert",
"Abhinav Valada",
"Thomas Brox"
] | [
"cs.RO",
"cs.LG"
] | [
"Computer Science"
] | 2025-07-02T00:00:00 | https://arxiv.org/abs/2507.02190 | https://arxiv.org/pdf/2507.02190v2 | 2507.02190 | 10.48550/arXiv.2507.02190 | 7 | 0 | false | null | arXiv.org | 0.2258 |
515ee8ab5ee2c80ca7bc8741b5c202afc5243c509ed37e982ccf9d2a7404bb21 | [
"arxiv",
"semantic_scholar"
] | TriVLA: A Triple-System-Based Unified Vision-Language-Action Model with Episodic World Modeling for General Robot Control | Recent advances in vision-language models (VLMs) have enabled robots to follow open-ended instructions and demonstrate impressive commonsense reasoning. However, current vision-language-action (VLA) frameworks primarily rely on static representations and limited temporal context, restricting agents to short-horizon, re... | [
"Zhenyang Liu",
"Yongchong Gu",
"Sixiao Zheng",
"Yanwei Fu",
"Xiangyang Xue",
"Yu-Gang Jiang"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-07-02T00:00:00 | https://arxiv.org/abs/2507.01424 | https://arxiv.org/pdf/2507.01424v3 | 2507.01424 | null | 4 | 0 | false | null | null | 0.1747 |
97ee28f28f6f5fb1e7cd19242dc2b4df3fbd1754e1d1c05a1d996a4ad47d964a | [
"arxiv",
"semantic_scholar"
] | Goal-VLA: Image-Generative VLMs as Object-Centric World Models Empowering Zero-shot Robot Manipulation | Generalization remains a fundamental challenge in robotic manipulation. To tackle this challenge, recent Vision-Language-Action (VLA) models build policies on top of Vision-Language Models (VLMs), seeking to transfer their open-world semantic knowledge. However, their zero-shot capability lags significantly behind the ... | [
"Haonan Chen",
"Jingxiang Guo",
"Bangjun Wang",
"Tianrui Zhang",
"Xuchuan Huang",
"Boren Zheng",
"Yiwen Hou",
"Chenrui Tie",
"Jiajun Deng",
"Lin Shao"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-06-30T00:00:00 | https://arxiv.org/abs/2506.23919 | https://arxiv.org/pdf/2506.23919v3 | 2506.23919 | null | 7 | 1 | false | null | null | 0.2258 |
24acf72974d84a3f9786b2b42529a5b502790db85796c03e41d1a5a91d4d15a4 | [
"arxiv",
"semantic_scholar"
] | CronusVLA: Towards Efficient and Robust Manipulation via Multi-Frame Vision-Language-Action Modeling | Recent vision-language-action (VLA) models built on pretrained vision-language models (VLMs) have demonstrated strong performance in robotic manipulation. However, these models remain constrained by the single-frame image paradigm and fail to fully leverage the temporal information offered by multi-frame histories, as ... | [
"Hao Li",
"Shuai Yang",
"Yilun Chen",
"Xinyi Chen",
"Xiaoda Yang",
"Yang Tian",
"Hanqing Wang",
"Tai Wang",
"Dahua Lin",
"Feng Zhao",
"Jiangmiao Pang"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-06-24T00:00:00 | https://arxiv.org/abs/2506.19816 | https://arxiv.org/pdf/2506.19816v2 | 2506.19816 | null | 12 | 1 | false | null | null | 0.2785 |
0c7169269f9cbeb841480f136452720146a408b39f6571c1d6f110f538e6b042 | [
"arxiv",
"semantic_scholar"
] | Unified Vision-Language-Action Model | Vision-language-action models (VLAs) have garnered significant attention for their potential in advancing robotic manipulation. However, previous approaches predominantly rely on the general comprehension capabilities of vision-language models (VLMs) to generate action signals, often overlooking the rich temporal and c... | [
"Yuqi Wang",
"Xinghang Li",
"Wenxuan Wang",
"Junbo Zhang",
"Yingyan Li",
"Yuntao Chen",
"Xinlong Wang",
"Zhaoxiang Zhang"
] | [
"cs.CV",
"cs.RO"
] | [
"Computer Science"
] | 2025-06-24T00:00:00 | https://arxiv.org/abs/2506.19850 | https://arxiv.org/pdf/2506.19850v1 | 2506.19850 | 10.48550/arXiv.2506.19850 | 101 | 11 | false | null | arXiv.org | 0.5396 |
eadba8ff04e6f8e945eb8bf0c57aeb7d46bf92ebee6c1460c1eee1fc073bd558 | [
"arxiv",
"semantic_scholar"
] | VLA-OS: Structuring and Dissecting Planning Representations and Paradigms in Vision-Language-Action Models | Recent studies on Vision-Language-Action (VLA) models have shifted from the end-to-end action-generation paradigm toward a pipeline involving task planning followed by action generation, demonstrating improved performance on various complex, long-horizon manipulation tasks. However, existing approaches vary significant... | [
"Chongkai Gao",
"Zixuan Liu",
"Zhenghao Chi",
"Junshan Huang",
"Xin Fei",
"Yiwen Hou",
"Yuxuan Zhang",
"Yudi Lin",
"Zhirui Fang",
"Zeyu Jiang",
"Lin Shao"
] | [
"cs.CV",
"cs.AI",
"cs.RO"
] | [
"Computer Science"
] | 2025-06-21T00:00:00 | https://arxiv.org/abs/2506.17561 | https://arxiv.org/pdf/2506.17561v1 | 2506.17561 | 10.48550/arXiv.2506.17561 | 29 | 4 | false | null | arXiv.org | 0.3693 |
3c6fea1e93f26ddb3072f4467de5ccab4184aad8a16d6fb2c9337f4ec7c02352 | [
"arxiv",
"semantic_scholar"
] | Distilling On-device Language Models for Robot Planning with Minimal Human Intervention | Large language models (LLMs) provide robots with powerful contextual reasoning abilities and a natural human interface. Yet, current LLM-enabled robots typically depend on cloud-hosted models, limiting their usability in environments with unreliable communication infrastructure, such as outdoor or industrial settings. ... | [
"Zachary Ravichandran",
"Ignacio Hounie",
"Fernando Cladera",
"Alejandro Ribeiro",
"George J. Pappas",
"Vijay Kumar"
] | [
"cs.RO",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-06-20T00:00:00 | https://arxiv.org/abs/2506.17486 | https://arxiv.org/pdf/2506.17486v2 | 2506.17486 | 10.48550/arXiv.2506.17486 | 13 | 0 | false | null | arXiv.org | 0.2865 |
b651c53996153c1ebd9b8922b8f10ff16d1d52954187dc7f57880135578d4739 | [
"arxiv",
"semantic_scholar"
] | Latent Action Diffusion for Cross-Embodiment Manipulation | End-to-end learning is emerging as a powerful paradigm for robotic manipulation, but its effectiveness is limited by data scarcity and the heterogeneity of action spaces across robot embodiments. In particular, diverse action spaces across different end-effectors create barriers for cross-embodiment learning and skill ... | [
"Erik Bauer",
"Elvis Nava",
"Robert K. Katzschmann"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-06-17T00:00:00 | https://arxiv.org/abs/2506.14608 | https://arxiv.org/pdf/2506.14608v4 | 2506.14608 | 10.48550/arXiv.2506.14608 | 18 | 1 | false | null | arXiv.org | 0.3197 |
393c83218c2d08a38e946985ddef78141b9f816a7d1b9933d3965f6cc4e92a78 | [
"arxiv",
"semantic_scholar"
] | Can Vision Language Models Understand Mimed Actions? | Nonverbal communication (NVC) plays an integral role in human language, but studying NVC in general is challenging because of its broad scope and high variance in interpretation among individuals and cultures. However, mime -- the theatrical technique of suggesting intent using only gesture, expression, and movement --... | [
"Hyundong Cho",
"Spencer Lin",
"Tejas Srinivasan",
"Michael Saxon",
"Deuksin Kwon",
"Natali T. Chavez",
"Jonathan May"
] | [
"cs.CL",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2025-06-17T00:00:00 | https://arxiv.org/abs/2506.21586 | https://arxiv.org/pdf/2506.21586v2 | 2506.21586 | 10.48550/arXiv.2506.21586 | 3 | 0 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.1982 |
d24d78eca7a82072706dd56d18d9844ccd3d36e79eec1ab12189d957a09ca717 | [
"arxiv",
"semantic_scholar"
] | GAF: Gaussian Action Field as a 4D Representation for Dynamic World Modeling in Robotic Manipulation | Accurate scene perception is critical for vision-based robotic manipulation. Existing approaches typically follow either a Vision-to-Action (V-A) paradigm, predicting actions directly from visual inputs, or a Vision-to-3D-to-Action (V-3D-A) paradigm, leveraging intermediate 3D representations. However, these methods of... | [
"Ying Chai",
"Litao Deng",
"Ruizhi Shao",
"Jiajun Zhang",
"Kangchen Lv",
"Liangjun Xing",
"Xiang Li",
"Hongwen Zhang",
"Yebin Liu"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-06-17T00:00:00 | https://arxiv.org/abs/2506.14135 | https://arxiv.org/pdf/2506.14135v5 | 2506.14135 | null | 3 | 0 | false | null | null | 0.1505 |
c9be3b70bf1a024264e96fc5ec5055101094603613aaa0413ea62f278f280f2d | [
"arxiv",
"semantic_scholar"
] | Can Pretrained Vision-Language Embeddings Alone Guide Robot Navigation? | Foundation models have revolutionized robotics by providing rich semantic representations without task-specific training. While many approaches integrate pretrained vision-language models (VLMs) with specialized navigation architectures, the fundamental question remains: can these pretrained embeddings alone successful... | [
"Nitesh Subedi",
"Adam Haroon",
"Shreyan Ganguly",
"Samuel T. K. Tetteh",
"Prajwal Koirala",
"Cody Fleming",
"Soumik Sarkar"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-06-17T00:00:00 | https://arxiv.org/abs/2506.14507 | https://arxiv.org/pdf/2506.14507v1 | 2506.14507 | 10.48550/arXiv.2506.14507 | 0 | 0 | true | https://github.com/oadamharoon/text2nav | arXiv.org | 0.3064 |
a9549b8076ed8b37305fd5eab6cf42a32f038d90eb2fd53873df2c83881a35e7 | [
"arxiv",
"semantic_scholar"
] | ROSA: Harnessing Robot States for Vision-Language and Action Alignment | Vision-Language-Action (VLA) models have recently made significant advance in multi-task, end-to-end robotic control, due to the strong generalization capabilities of Vision-Language Models (VLMs). A fundamental challenge in developing such models is effectively aligning the vision-language space with the robotic actio... | [
"Yuqing Wen",
"Kefan Gu",
"Haoxuan Liu",
"Yucheng Zhao",
"Tiancai Wang",
"Haoqiang Fan",
"Xiaoyan Sun"
] | [
"cs.RO",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2025-06-16T00:00:00 | https://arxiv.org/abs/2506.13679 | https://arxiv.org/pdf/2506.13679v1 | 2506.13679 | 10.48550/arXiv.2506.13679 | 4 | 0 | false | null | arXiv.org | 0.1971 |
efd294e8db46a9220140feed62724f638575b312db020ad3920ddf95ef413f96 | [
"arxiv",
"semantic_scholar"
] | Gondola: Grounded Vision Language Planning for Generalizable Robotic Manipulation | Robotic manipulation faces a significant challenge in generalizing across unseen objects, environments and tasks specified by diverse language instructions. To improve generalization capabilities, recent research has incorporated large language models (LLMs) for planning and action execution. While promising, these met... | [
"Shizhe Chen",
"Ricardo Garcia",
"Paul Pacaud",
"Cordelia Schmid"
] | [
"cs.RO",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2025-06-12T00:00:00 | https://arxiv.org/abs/2506.11261 | https://arxiv.org/pdf/2506.11261v1 | 2506.11261 | 10.48550/arXiv.2506.11261 | 1 | 0 | false | null | arXiv.org | 0.1925 |
88210d925b4169ef81b163bad4f3dd6a8dbd06e78d726934d28232f0523113ff | [
"arxiv",
"semantic_scholar"
] | SAFE: Multitask Failure Detection for Vision-Language-Action Models | While vision-language-action models (VLAs) have shown promising robotic behaviors across a diverse set of manipulation tasks, they achieve limited success rates when deployed on novel tasks out of the box. To allow these policies to safely interact with their environments, we need a failure detector that gives a timely... | [
"Qiao Gu",
"Yuanliang Ju",
"Shengxiang Sun",
"Igor Gilitschenski",
"Haruki Nishimura",
"Masha Itkina",
"Florian Shkurti"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2025-06-11T00:00:00 | https://arxiv.org/abs/2506.09937 | https://arxiv.org/pdf/2506.09937v2 | 2506.09937 | 10.48550/arXiv.2506.09937 | 44 | 6 | false | null | arXiv.org | 0.4225 |
2838c4dbee3fd7082a0d0b26cf986f4f86a2f475376698624b1c32fabadb7230 | [
"arxiv",
"semantic_scholar"
] | From Intention to Execution: Probing the Generalization Boundaries of Vision-Language-Action Models | One promise that Vision-Language-Action (VLA) models hold over traditional imitation learning for robotics is to leverage the broad generalization capabilities of large Vision-Language Models (VLMs) to produce versatile, "generalist" robot policies. However, current evaluations of VLAs remain insufficient. Traditional ... | [
"Irving Fang",
"Juexiao Zhang",
"Shengbang Tong",
"Chen Feng"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-06-11T00:00:00 | https://arxiv.org/abs/2506.09930 | https://arxiv.org/pdf/2506.09930v1 | 2506.09930 | 10.48550/arXiv.2506.09930 | 17 | 2 | false | null | arXiv.org | 0.3138 |
d39e281bb7a5d768295444ead3ad5f4d0b3631d0d3fafdeb9d2e28a28fa4bf4c | [
"arxiv",
"semantic_scholar"
] | Help or Hindrance: Understanding the Impact of Robot Communication in Action Teams | The human-robot interaction (HRI) field has recognized the importance of enabling robots to interact with teams. Human teams rely on effective communication for successful collaboration in time-sensitive environments. Robots can play a role in enhancing team coordination through real-time assistance. Despite significan... | [
"Tauhid Tanjim",
"Jonathan St. George",
"Kevin Ching",
"Angelique Taylor"
] | [
"cs.HC",
"cs.RO"
] | [
"Computer Science"
] | 2025-06-10T00:00:00 | https://arxiv.org/abs/2506.08892 | https://arxiv.org/pdf/2506.08892v3 | 2506.08892 | 10.1109/RO-MAN63969.2025.11217909 | 3 | 0 | false | null | IEEE International Symposium on Robot and Human Interactive Communication | 0.1902 |
55470e74bc74ced6a6f89a42f5bafe81fbf77b0ce5692d7f388844ab59c56f82 | [
"arxiv",
"semantic_scholar"
] | BitVLA: 1-bit Vision-Language-Action Models for Robotics Manipulation | Deploying powerful Vision-Language-Action (VLA) models on edge devices is limited by their massive size. In this paper, we take a deployment-oriented view of VLA training: we target efficiency through model design and optimization, rather than relying solely on post-hoc compression. Thus, we propose BitVLA, a fully nat... | [
"Hongyu Wang",
"Chuyan Xiong",
"Ruiping Wang",
"Xilin Chen"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-06-09T00:00:00 | https://arxiv.org/abs/2506.07530 | https://arxiv.org/pdf/2506.07530v2 | 2506.07530 | 10.48550/arXiv.2506.07530 | 26 | 4 | true | https://github.com/ustcwhy/BitVLA | arXiv.org | 0.3578 |
46de50ebc145803377d6469172283f19da113d465ff9191da0e05e7a82d9cd4f | [
"arxiv",
"semantic_scholar"
] | Coordinated Robustness Evaluation Framework for Vision-Language Models | Vision-language models, which integrate computer vision and natural language processing capabilities, have demonstrated significant advancements in tasks such as image captioning and visual question and answering. However, similar to traditional models, they are susceptible to small perturbations, posing a challenge to... | [
"Ashwin Ramesh Babu",
"Sajad Mousavi",
"Vineet Gundecha",
"Sahand Ghorbanpour",
"Avisek Naug",
"Antonio Guillen",
"Ricardo Luna Gutierrez",
"Soumyendu Sarkar"
] | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-06-05T00:00:00 | https://arxiv.org/abs/2506.05429 | https://arxiv.org/pdf/2506.05429v1 | 2506.05429 | 10.48550/arXiv.2506.05429 | 0 | 0 | false | null | null | 0.1174 |
0972000a827405fa180a4d50b655f638a7e6af5f6646c8812adc7676e77b2f21 | [
"arxiv",
"semantic_scholar"
] | Generating 6DoF Object Manipulation Trajectories from Action Description in Egocentric Vision | Learning to use tools or objects in common scenes, particularly handling them in various ways as instructed, is a key challenge for developing interactive robots. Training models to generate such manipulation trajectories requires a large and diverse collection of detailed manipulation demonstrations for various object... | [
"Tomoya Yoshida",
"Shuhei Kurita",
"Taichi Nishimura",
"Shinsuke Mori"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-06-04T00:00:00 | https://arxiv.org/abs/2506.03605 | https://arxiv.org/pdf/2506.03605v1 | 2506.03605 | 10.1109/CVPR52734.2025.01619 | 5 | 1 | false | null | Computer Vision and Pattern Recognition | 0.1945 |
f9455e1dd7c689114f5e4fc32b129590e39fc0b154eb891d1f8f098013f5cd3d | [
"arxiv",
"semantic_scholar"
] | Adversarial Attacks on Robotic Vision Language Action Models | The emergence of vision-language-action models (VLAs) for end-to-end control is reshaping the field of robotics by enabling the fusion of multimodal sensory inputs at the billion-parameter scale. The capabilities of VLAs stem primarily from their architectures, which are often based on frontier large language models (L... | [
"Eliot Krzysztof Jones",
"Alexander Robey",
"Andy Zou",
"Zachary Ravichandran",
"George J. Pappas",
"Hamed Hassani",
"Matt Fredrikson",
"J. Zico Kolter"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2025-06-03T00:00:00 | https://arxiv.org/abs/2506.03350 | https://arxiv.org/pdf/2506.03350v1 | 2506.03350 | 10.48550/arXiv.2506.03350 | 26 | 6 | true | https://github.com/eliotjones1/robogcg | arXiv.org | 0.4225 |
5f057caa6314d0830d0c80a9bbc59cfdfda5e403a878a015d40153a8a8cf1caf | [
"arxiv",
"semantic_scholar"
] | SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics | Vision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into vision-language-action (VLA) models that enable natural language-dr... | [
"Mustafa Shukor",
"Dana Aubakirova",
"Francesco Capuano",
"Pepijn Kooijmans",
"Steven Palma",
"Adil Zouitine",
"Michel Aractingi",
"Caroline Pascal",
"Martino Russi",
"Andres Marafioti",
"Simon Alibert",
"Matthieu Cord",
"Thomas Wolf",
"Remi Cadene"
] | [
"cs.LG",
"cs.RO"
] | [
"Computer Science"
] | 2025-06-02T00:00:00 | https://arxiv.org/abs/2506.01844 | https://arxiv.org/pdf/2506.01844v1 | 2506.01844 | 10.48550/arXiv.2506.01844 | 333 | 51 | true | https://github.com/huggingface/lerobot | arXiv.org | 0.858 |
6f06797e063a3c7e66abe6162539e4e1d03da0d65908549819382521aacaa880 | [
"arxiv",
"semantic_scholar"
] | OG-VLA: Orthographic Image Generation for 3D-Aware Vision-Language Action Model | We introduce OG-VLA, a novel architecture and learning framework that combines the generalization strengths of Vision Language Action models (VLAs) with the robustness of 3D-aware policies. We address the challenge of mapping natural language instructions and one or more RGBD observations to quasi-static robot actions.... | [
"Ishika Singh",
"Ankit Goyal",
"Stan Birchfield",
"Dieter Fox",
"Animesh Garg",
"Valts Blukis"
] | [
"cs.RO",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2025-06-01T00:00:00 | https://arxiv.org/abs/2506.01196 | https://arxiv.org/pdf/2506.01196v2 | 2506.01196 | null | 9 | 0 | false | null | null | 0.25 |
56690dc8a0dc15e1e0408ff5bbdc0e3d81601bb8b6e6be2799ade6d9805f77fb | [
"arxiv",
"semantic_scholar"
] | LoHoVLA: A Unified Vision-Language-Action Model for Long-Horizon Embodied Tasks | Real-world embodied agents face long-horizon tasks, characterized by high-level goals demanding multi-step solutions beyond single actions. Successfully navigating these requires both high-level task planning (i.e., decomposing goals into sub-tasks) and low-level motion control (i.e., generating precise robot actions).... | [
"Yi Yang",
"Jiaxuan Sun",
"Siqi Kou",
"Yihan Wang",
"Zhijie Deng"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-31T00:00:00 | https://arxiv.org/abs/2506.00411 | https://arxiv.org/pdf/2506.00411v1 | 2506.00411 | 10.48550/arXiv.2506.00411 | 24 | 1 | false | null | arXiv.org | 0.3495 |
79b67d6c63f9419670385da0d81fa807696f73c4305173b8c6e05c52558c702a | [
"arxiv",
"semantic_scholar"
] | Impromptu VLA: Open Weights and Open Data for Driving Vision-Language-Action Models | Vision-Language-Action (VLA) models for autonomous driving show promise but falter in unstructured corner case scenarios, largely due to a scarcity of targeted benchmarks. To address this, we introduce Impromptu VLA. Our core contribution is the Impromptu VLA Dataset: over 80,000 meticulously curated video clips, disti... | [
"Haohan Chi",
"Huan-ang Gao",
"Ziming Liu",
"Jianing Liu",
"Chenyu Liu",
"Jinwei Li",
"Kaisen Yang",
"Yangcheng Yu",
"Zeda Wang",
"Wenyi Li",
"Leichen Wang",
"Xingtao Hu",
"Hao Sun",
"Hang Zhao",
"Hao Zhao"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-05-29T00:00:00 | https://arxiv.org/abs/2505.23757 | https://arxiv.org/pdf/2505.23757v1 | 2505.23757 | 10.48550/arXiv.2505.23757 | 51 | 10 | true | https://github.com/ahydchh/Impromptu-VLA | arXiv.org | 0.5207 |
1dea84baffb26b9b82980d7fd1a72b5c64b8990ebd852b36b74850eeec9020c6 | [
"arxiv",
"semantic_scholar"
] | ChatVLA-2: Vision-Language-Action Model with Open-World Embodied Reasoning from Pretrained Knowledge | Vision-language-action (VLA) models have emerged as the next generation of models in robotics. However, despite leveraging powerful pre-trained Vision-Language Models (VLMs), existing end-to-end VLA systems often lose key capabilities during fine-tuning as the model adapts to specific robotic tasks. We argue that a gen... | [
"Zhongyi Zhou",
"Yichen Zhu",
"Junjie Wen",
"Chaomin Shen",
"Yi Xu"
] | [
"cs.RO",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2025-05-28T00:00:00 | https://arxiv.org/abs/2505.21906 | https://arxiv.org/pdf/2505.21906v2 | 2505.21906 | 10.48550/arXiv.2505.21906 | 56 | 9 | false | null | arXiv.org | 0.5 |
e98ac0fc0f870fc1866a45fc347295b23cab3c7556f6d7cc8036cd08cb2cf88f | [
"arxiv",
"semantic_scholar"
] | ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation | Vision-Language-Action (VLA) models have advanced general-purpose robotic manipulation by leveraging pretrained visual and linguistic representations. However, they struggle with contact-rich tasks that require fine-grained control involving force, especially under visual occlusion or dynamic uncertainty. To address th... | [
"Jiawen Yu",
"Hairuo Liu",
"Qiaojun Yu",
"Jieji Ren",
"Ce Hao",
"Haitong Ding",
"Guangyu Huang",
"Guofan Huang",
"Yan Song",
"Panpan Cai",
"Cewu Lu",
"Wenqiang Zhang"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-05-28T00:00:00 | https://arxiv.org/abs/2505.22159 | https://arxiv.org/pdf/2505.22159v3 | 2505.22159 | 10.48550/arXiv.2505.22159 | 75 | 3 | false | null | arXiv.org | 0.4702 |
f06261fa08f2583474e34c2cd2db698ec0977e98dedaaa3e042fd0c5c2fb8de0 | [
"arxiv",
"semantic_scholar"
] | Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review | Rapid advancements in foundation models, including Large Language Models, Vision-Language Models, Multimodal Large Language Models, and Vision-Language-Action Models, have opened new avenues for embodied AI in mobile service robotics. By combining foundation models with the principles of embodied AI, where intelligent ... | [
"Matthew Lisondra",
"Beno Benhabib",
"Goldie Nejat"
] | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-26T00:00:00 | https://arxiv.org/abs/2505.20503 | https://arxiv.org/pdf/2505.20503v2 | 2505.20503 | 10.3390/robotics15030055 | 13 | 0 | false | null | Robotics | 0.2865 |
8b6286b89fd7b0f6a7d062faaece060edc21c827d188160d915a36445bec161b | [
"arxiv",
"semantic_scholar"
] | VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning | Recent high-capacity vision-language-action (VLA) models have demonstrated impressive performance on a range of robotic manipulation tasks by imitating human demonstrations. However, exploiting offline data with limited visited states will cause execution failure in out-of-distribution scenarios. Intuitively, an explor... | [
"Guanxing Lu",
"Wenkai Guo",
"Chubin Zhang",
"Yuheng Zhou",
"Haonan Jiang",
"Zifeng Gao",
"Yansong Tang",
"Ziwei Wang"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-24T00:00:00 | https://arxiv.org/abs/2505.18719 | https://arxiv.org/pdf/2505.18719v1 | 2505.18719 | 10.48550/arXiv.2505.18719 | 133 | 17 | false | null | arXiv.org | 0.6276 |
acabf051dc019876045efc71720f217fb3ab282fc04eb118b8bc90cf8a1d0739 | [
"arxiv",
"semantic_scholar"
] | ManipLVM-R1: Reinforcement Learning for Reasoning in Embodied Manipulation with Large Vision-Language Models | Large Vision-Language Models (LVLMs) have recently advanced robotic manipulation by leveraging vision for scene perception and language for instruction following. However, existing methods rely heavily on costly human-annotated training datasets, which limits their generalization and causes them to struggle in out-of-d... | [
"Zirui Song",
"Guangxian Ouyang",
"Mingzhe Li",
"Yuheng Ji",
"Chenxi Wang",
"Zixiang Xu",
"Zeyu Zhang",
"Xiaoqing Zhang",
"Qian Jiang",
"Zhenhao Chen",
"Zhongzhi Li",
"Rui Yan",
"Xiuying Chen"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-05-22T00:00:00 | https://arxiv.org/abs/2505.16517 | https://arxiv.org/pdf/2505.16517v2 | 2505.16517 | 10.48550/arXiv.2505.16517 | 23 | 5 | false | null | AAAI Conference on Artificial Intelligence | 0.3891 |
352fc05cbba1cb01b0d0c908ae01e492316eb4736a07b113ab6813c9681337ee | [
"arxiv",
"semantic_scholar"
] | Exploring the Limits of Vision-Language-Action Manipulations in Cross-task Generalization | The generalization capabilities of vision-language-action (VLA) models to unseen tasks are crucial to achieving general-purpose robotic manipulation in open-world settings. However, the cross-task generalization capabilities of existing VLA models remain significantly underexplored. To address this gap, we introduce AG... | [
"Jiaming Zhou",
"Ke Ye",
"Jiayi Liu",
"Teli Ma",
"Zifan Wang",
"Ronghe Qiu",
"Kun-Yu Lin",
"Zhilin Zhao",
"Junwei Liang"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-05-21T00:00:00 | https://arxiv.org/abs/2505.15660 | https://arxiv.org/pdf/2505.15660v3 | 2505.15660 | 10.48550/arXiv.2505.15660 | 29 | 1 | false | null | arXiv.org | 0.3693 |
575c5130d924b35e51ec2fa63d336e6c7c532c784f51eb8c51781ba53f1fe61a | [
"arxiv",
"semantic_scholar"
] | From Grounding to Manipulation: Case Studies of Foundation Model Integration in Embodied Robotic Systems | Foundation models (FMs) are increasingly used to bridge language and action in embodied agents, yet the operational characteristics of different FM integration strategies remain under-explored -- particularly for complex instruction following and versatile action generation in changing environments. This paper examines... | [
"Xiuchao Sui",
"Daiying Tian",
"Qi Sun",
"Ruirui Chen",
"Dongkyu Choi",
"Kenneth Kwok",
"Soujanya Poria"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-05-21T00:00:00 | https://arxiv.org/abs/2505.15685 | https://arxiv.org/pdf/2505.15685v2 | 2505.15685 | 10.48550/arXiv.2505.15685 | 6 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.2113 |
3d1ed916d43443d4c2d3210fa862f73c8713b066943cf3617b05655b85f00c54 | [
"arxiv",
"semantic_scholar"
] | ManipBench: Benchmarking Vision-Language Models for Low-Level Robot Manipulation | Vision-Language Models (VLMs) have revolutionized artificial intelligence and robotics due to their commonsense reasoning capabilities. In robotic manipulation, VLMs are used primarily as high-level planners, but recent work has also studied their lower-level reasoning ability, which refers to making decisions about pr... | [
"Enyu Zhao",
"Vedant Raval",
"Hejia Zhang",
"Jiageng Mao",
"Zeyu Shangguan",
"Stefanos Nikolaidis",
"Yue Wang",
"Daniel Seita"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2025-05-14T00:00:00 | https://arxiv.org/abs/2505.09698 | https://arxiv.org/pdf/2505.09698v2 | 2505.09698 | 10.48550/arXiv.2505.09698 | 23 | 0 | false | null | arXiv.org | 0.3451 |
c45c8adeec45fcd4d01f9f6f47c242f6647831c41b585930762842d8fdf75067 | [
"arxiv",
"semantic_scholar"
] | VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation | While vision-language models have advanced significantly, their application in language-conditioned robotic manipulation is still underexplored, especially for contact-rich tasks that extend beyond visually dominant pick-and-place scenarios. To bridge this gap, we introduce Vision-Tactile-Language-Action model, a novel... | [
"Chaofan Zhang",
"Peng Hao",
"Xiaoge Cao",
"Xiaoshuai Hao",
"Shaowei Cui",
"Shuo Wang"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-05-14T00:00:00 | https://arxiv.org/abs/2505.09577 | https://arxiv.org/pdf/2505.09577v1 | 2505.09577 | 10.48550/arXiv.2505.09577 | 58 | 2 | false | null | arXiv.org | 0.4427 |
cedfbe057d971abe2dbd3572925908545033127235f9c75b497103de56492427 | [
"arxiv",
"semantic_scholar"
] | From Seeing to Doing: Bridging Reasoning and Decision for Robotic Manipulation | Achieving generalization in robotic manipulation remains a critical challenge, particularly for unseen scenarios and novel tasks. Current Vision-Language-Action (VLA) models, while building on top of general Vision-Language Models (VLMs), still fall short of achieving robust zero-shot performance due to the scarcity an... | [
"Yifu Yuan",
"Haiqin Cui",
"Yibin Chen",
"Zibin Dong",
"Fei Ni",
"Longxin Kou",
"Jinyi Liu",
"Pengyi Li",
"Yan Zheng",
"Jianye Hao"
] | [
"cs.RO",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-13T00:00:00 | https://arxiv.org/abs/2505.08548 | https://arxiv.org/pdf/2505.08548v3 | 2505.08548 | 10.48550/arXiv.2505.08548 | 28 | 1 | false | null | arXiv.org | 0.3656 |
d004ade0677d6fe738cdfc09d3db9a17ca67ea4e289d513c695b410d71ee14f9 | [
"arxiv",
"semantic_scholar"
] | Towards Embodiment Scaling Laws in Robot Locomotion | Cross-embodiment generalization underpins the vision of building generalist embodied agents for any robot, yet its enabling factors remain poorly understood. We investigate embodiment scaling laws, the hypothesis that increasing the number of training embodiments improves generalization to unseen ones, using robot loco... | [
"Bo Ai",
"Liu Dai",
"Nico Bohlinger",
"Dichen Li",
"Tongzhou Mu",
"Zhanxin Wu",
"K. Fay",
"Henrik I. Christensen",
"Jan Peters",
"Hao Su"
] | [
"cs.RO",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2025-05-09T00:00:00 | https://arxiv.org/abs/2505.05753 | https://arxiv.org/pdf/2505.05753v2 | 2505.05753 | 10.48550/arXiv.2505.05753 | 15 | 0 | false | null | arXiv.org | 0.301 |
7a759f3af8ce40ea80ea2010a6ebb9cf719be742a14c4affcb52546f3e1e3880 | [
"arxiv",
"semantic_scholar"
] | 3D CAVLA: Leveraging Depth and 3D Context to Generalize Vision Language Action Models for Unseen Tasks | Robotic manipulation in 3D requires effective computation of N degree-of-freedom joint-space trajectories that enable precise and robust control. To achieve this, robots must integrate semantic understanding with visual perception to transform real-world observations into low-level control for object interaction. Recen... | [
"Vineet Bhat",
"Yu-Hsiang Lan",
"Prashanth Krishnamurthy",
"Ramesh Karri",
"Farshad Khorrami"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-05-09T00:00:00 | https://arxiv.org/abs/2505.05800 | https://arxiv.org/pdf/2505.05800v2 | 2505.05800 | 10.48550/arXiv.2505.05800 | 26 | 5 | true | null | arXiv.org | 0.3891 |
3096b360cecb4df3924952c1b1b1b5c13f7a82e8ba7ef1f564f5757b94b75f96 | [
"arxiv",
"semantic_scholar"
] | Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges | Vision-Language-Action (VLA) models mark a transformative advancement in artificial intelligence, aiming to unify perception, natural language understanding, and embodied action within a single computational framework. This foundational review presents a comprehensive synthesis of recent advancements in Vision-Language... | [
"Ranjan Sapkota",
"Yang Cao",
"Konstantinos I. Roumeliotis",
"Manoj Karkee"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-05-07T00:00:00 | https://arxiv.org/abs/2505.04769 | https://arxiv.org/pdf/2505.04769v2 | 2505.04769 | null | 91 | 3 | true | https://github.com/Applied-AI-Research-Lab/Vision-Language-Action-Models-Concepts-Progress-Applications-and-Challenges | null | 0.4909 |
a3b70d68072968a5c13203f7d24cc535ee897e18d05872cbb50f6f7e5a926372 | [
"arxiv",
"semantic_scholar"
] | RoboGround: Robotic Manipulation with Grounded Vision-Language Priors | Recent advancements in robotic manipulation have highlighted the potential of intermediate representations for improving policy generalization. In this work, we explore grounding masks as an effective intermediate representation, balancing two key advantages: (1) effective spatial guidance that specifies target objects... | [
"Haifeng Huang",
"Xinyi Chen",
"Yilun Chen",
"Hao Li",
"Xiaoshen Han",
"Zehan Wang",
"Tai Wang",
"Jiangmiao Pang",
"Zhou Zhao"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-04-30T00:00:00 | https://arxiv.org/abs/2504.21530 | https://arxiv.org/pdf/2504.21530v1 | 2504.21530 | 10.1109/CVPR52734.2025.02099 | 42 | 2 | false | null | Computer Vision and Pattern Recognition | 0.4084 |
b6e51f3c77132066b8a2f457079bcc632c0bfea46adb223ca62a31eb59bc2cf8 | [
"arxiv",
"semantic_scholar"
] | Benchmarking Vision Language Models on German Factual Data | Similar to LLMs, the development of vision language models is mainly driven by English datasets and models trained in English and Chinese language, whereas support for other languages, even those considered high-resource languages such as German, remains significantly weaker. In this work we present an analysis of open... | [
"René Peinl",
"Vincent Tischler"
] | [
"cs.CL"
] | [
"Computer Science"
] | 2025-04-15T00:00:00 | https://arxiv.org/abs/2504.11108 | https://arxiv.org/pdf/2504.11108v2 | 2504.11108 | 10.48550/arXiv.2504.11108 | 4 | 0 | false | null | Artificial Intelligence Applications and Innovations | 0.1747 |
1794133cb67059f451af4a7ec27338d07bc95ad0701041bb64a9d715587a8294 | [
"arxiv",
"semantic_scholar"
] | Vision-Language Model for Object Detection and Segmentation: A Review and Evaluation | Vision-Language Model (VLM) have gained widespread adoption in Open-Vocabulary (OV) object detection and segmentation tasks. Despite they have shown promise on OV-related tasks, their effectiveness in conventional vision tasks has thus far been unevaluated. In this work, we present the systematic review of VLM-based de... | [
"Yongchao Feng",
"Yajie Liu",
"Shuai Yang",
"Wenrui Cai",
"Jinqing Zhang",
"Qiqi Zhan",
"Ziyue Huang",
"Hongxi Yan",
"Qiao Wan",
"Chenguang Liu",
"Junzhe Wang",
"Jiahui Lv",
"Ziqi Liu",
"Tengyuan Shi",
"Qingjie Liu",
"Yunhong Wang"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2025-04-13T00:00:00 | https://arxiv.org/abs/2504.09480 | https://arxiv.org/pdf/2504.09480v1 | 2504.09480 | 10.48550/arXiv.2504.09480 | 21 | 0 | true | https://github.com/better-chao/perceptual_abilities_evaluation | arXiv.org | 0.3356 |
2cc2155bf2cad7239410fcf8b8514d34bfd3d4a70dcbeeb6d4a934c9d66c80b1 | [
"arxiv",
"semantic_scholar"
] | Humanoid Agent via Embodied Chain-of-Action Reasoning with Multimodal Foundation Models for Zero-Shot Loco-Manipulation | Humanoid loco-manipulation, which integrates whole-body locomotion with dexterous manipulation, remains a fundamental challenge in robotics. Beyond whole-body coordination and balance, a central difficulty lies in understanding human instructions and translating them into coherent sequences of embodied actions. Recent ... | [
"Congcong Wen",
"Geeta Chandra Raju Bethala",
"Yu Hao",
"Niraj Pudasaini",
"Hao Huang",
"Shuaihang Yuan",
"Baoru Huang",
"Anh Nguyen",
"Mengyu Wang",
"Anthony Tzes",
"Yi Fang"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2025-04-13T00:00:00 | https://arxiv.org/abs/2504.09532 | https://arxiv.org/pdf/2504.09532v3 | 2504.09532 | null | 1 | 0 | false | null | null | 0.0788 |
5e75b38b86d2373b4059b9ab747bba6aa10ba5f37527efce334f1b75013bcf2c | [
"arxiv",
"semantic_scholar"
] | Leveraging Passive Compliance of Soft Robotics for Physical Human-Robot Collaborative Manipulation | This work represents an initial benchmark of a large-scale soft robot performing physical, collaborative manipulation of a long, extended object with a human partner. The robot consists of a pneumatically-actuated, three-link continuum soft manipulator mounted to an omni-directional mobile base. The system level config... | [
"Dallin L. Cordon",
"Shaden Moss",
"Marc Killpack",
"John L. Salmon"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-04-11T00:00:00 | https://arxiv.org/abs/2504.08184 | https://arxiv.org/pdf/2504.08184v1 | 2504.08184 | 10.48550/arXiv.2504.08184 | 1 | 0 | false | null | arXiv.org | 0.1215 |
694b77d7aeda0d9fdbfdcdbe988d6a31894c7f78f7f3c6da5ebc694575271b5f | [
"arxiv",
"semantic_scholar"
] | Multimodal Fusion and Vision-Language Models: A Survey for Robot Vision | Robot vision has greatly benefited from advancements in multimodal fusion techniques and vision-language models (VLMs). We adopt a task-oriented perspective to systematically review the applications and advancements of multimodal fusion methods and VLMs in the field of robot vision. For semantic scene understanding tas... | [
"Xiaofeng Han",
"Shunpeng Chen",
"Zenghuang Fu",
"Zhe Feng",
"Lue Fan",
"Dong An",
"Changwei Wang",
"Li Guo",
"Weiliang Meng",
"Xiaopeng Zhang",
"Rongtao Xu",
"Shibiao Xu"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-04-03T00:00:00 | https://arxiv.org/abs/2504.02477 | https://arxiv.org/pdf/2504.02477v3 | 2504.02477 | 10.1016/j.inffus.2025.103652 | 72 | 2 | true | https://github.com/Xiaofeng-Han-Res/MF-RV | Information Fusion | 0.4658 |
a808c026e248d0ccf6b9352e3884e34203a14ca07b4833b79fa1c13e66b6bfc3 | [
"arxiv",
"semantic_scholar"
] | CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models | Vision-language-action models (VLAs) have shown potential in leveraging pretrained vision-language models and diverse robot demonstrations for learning generalizable sensorimotor control. While this paradigm effectively utilizes large-scale data from both robotic and non-robotic sources, current VLAs primarily focus on... | [
"Qingqing Zhao",
"Yao Lu",
"Moo Jin Kim",
"Zipeng Fu",
"Zhuoyang Zhang",
"Yecheng Wu",
"Zhaoshuo Li",
"Qianli Ma",
"Song Han",
"Chelsea Finn",
"Ankur Handa",
"Ming-Yu Liu",
"Donglai Xiang",
"Gordon Wetzstein",
"Tsung-Yi Lin"
] | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | [
"Computer Science"
] | 2025-03-27T00:00:00 | https://arxiv.org/abs/2503.22020 | https://arxiv.org/pdf/2503.22020v1 | 2503.22020 | 10.1109/CVPR52734.2025.00166 | 436 | 33 | false | null | Computer Vision and Pattern Recognition | 0.7657 |
821ee1cc8c1434c0619135bd61b4214eefca6e79fc953982a09c82e1b173ebe5 | [
"arxiv",
"semantic_scholar"
] | How do language models learn facts? Dynamics, curricula and hallucinations | Large language models accumulate vast knowledge during pre-training, yet the dynamics governing this acquisition remain poorly understood. This work investigates the learning dynamics of language models on a synthetic factual recall task, uncovering three key findings: First, language models learn in three phases, exhi... | [
"Nicolas Zucchet",
"Jörg Bornschein",
"Stephanie Chan",
"Andrew Lampinen",
"Razvan Pascanu",
"Soham De"
] | [
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-27T00:00:00 | https://arxiv.org/abs/2503.21676 | https://arxiv.org/pdf/2503.21676v2 | 2503.21676 | 10.48550/arXiv.2503.21676 | 28 | 2 | false | null | arXiv.org | 0.3656 |
2b80b9f1feb26ae9d01f85479ac6a22251d9b81213b454e5adea16f0dda42861 | [
"arxiv",
"semantic_scholar"
] | MoLe-VLA: Dynamic Layer-skipping Vision Language Action Model via Mixture-of-Layers for Efficient Robot Manipulation | Multimodal Large Language Models (MLLMs) excel in understanding complex language and visual data, enabling generalist robotic systems to interpret instructions and perform embodied tasks. Nevertheless, their real-world deployment is hindered by substantial computational and storage demands. Recent insights into the hom... | [
"Rongyu Zhang",
"Menghang Dong",
"Yuan Zhang",
"Liang Heng",
"Xiaowei Chi",
"Gaole Dai",
"Li Du",
"Yuan Du",
"Shanghang Zhang"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2025-03-26T00:00:00 | https://arxiv.org/abs/2503.20384 | https://arxiv.org/pdf/2503.20384v2 | 2503.20384 | 10.48550/arXiv.2503.20384 | 64 | 4 | false | null | AAAI Conference on Artificial Intelligence | 0.4532 |
77f03ca3617de0aacface295f48fbf957491d6aeafb707d97ba3fa6a01ce1c77 | [
"arxiv",
"semantic_scholar"
] | RoboFlamingo-Plus: Fusion of Depth and RGB Perception with Vision-Language Models for Enhanced Robotic Manipulation | As robotic technologies advancing towards more complex multimodal interactions and manipulation tasks, the integration of advanced Vision-Language Models (VLMs) has become a key driver in the field. Despite progress with current methods, challenges persist in fusing depth and RGB information within 3D environments and ... | [
"Sheng Wang"
] | [
"cs.RO",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2025-03-25T00:00:00 | https://arxiv.org/abs/2503.19510 | https://arxiv.org/pdf/2503.19510v1 | 2503.19510 | 10.1109/RCAR65431.2025.11139480 | 15 | 0 | false | null | International Conference on Real-time Computing and Robotics | 0.301 |
3dce6e1d9af0d5db67856b81d61d1fde44ae9d374bb83b2066f34a73ab24e446 | [
"arxiv",
"semantic_scholar"
] | IRef-VLA: A Benchmark for Interactive Referential Grounding with Imperfect Language in 3D Scenes | With the recent rise of large language models, vision-language models, and other general foundation models, there is growing potential for multimodal, multi-task robotics that can operate in diverse environments given natural language input. One such application is indoor navigation using natural language instructions.... | [
"Haochen Zhang",
"Nader Zantout",
"Pujith Kachana",
"Ji Zhang",
"Wenshan Wang"
] | [
"cs.CV",
"cs.RO"
] | [
"Computer Science"
] | 2025-03-20T00:00:00 | https://arxiv.org/abs/2503.17406 | https://arxiv.org/pdf/2503.17406v1 | 2503.17406 | 10.1109/ICRA55743.2025.11127464 | 5 | 0 | true | https://github.com/HaochenZ11/IRef-VLA | IEEE International Conference on Robotics and Automation | 0.1945 |
fbaeb6e91db5d8309de58b4f96dcea865969a1e1056d92be5befdcd1a82b8de1 | [
"arxiv",
"semantic_scholar"
] | Vision-Language Embodiment for Monocular Depth Estimation | Depth estimation is a core problem in robotic perception and vision tasks, but 3D reconstruction from a single image presents inherent uncertainties. Current depth estimation models primarily rely on inter-image relationships for supervised training, often overlooking the intrinsic information provided by the camera it... | [
"Jinchang Zhang",
"Guoyu Lu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2025-03-18T00:00:00 | https://arxiv.org/abs/2503.16535 | https://arxiv.org/pdf/2503.16535v2 | 2503.16535 | 10.1109/CVPR52734.2025.02745 | 11 | 0 | false | null | Computer Vision and Pattern Recognition | 0.2698 |
4cd4abc45c3ae444d199e9d3faa3bcb8f5cd78f2d2a3b6a85152d972285bda55 | [
"arxiv",
"semantic_scholar"
] | MoManipVLA: Transferring Vision-language-action Models for General Mobile Manipulation | Mobile manipulation is the fundamental challenge for robotics to assist humans with diverse tasks and environments in everyday life. However, conventional mobile manipulation approaches often struggle to generalize across different tasks and environments because of the lack of large-scale training. In contrast, recent ... | [
"Zhenyu Wu",
"Yuheng Zhou",
"Xiuwei Xu",
"Ziwei Wang",
"Haibin Yan"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-03-17T00:00:00 | https://arxiv.org/abs/2503.13446 | https://arxiv.org/pdf/2503.13446v1 | 2503.13446 | 10.1109/CVPR52734.2025.00167 | 42 | 4 | false | null | Computer Vision and Pattern Recognition | 0.4084 |
4ac9dfc1334f1559bba7adb8a0a1d13d836d11cb878e7ae143038a1c9b93bdcf | [
"arxiv",
"semantic_scholar"
] | HybridVLA: Collaborative Diffusion and Autoregression in a Unified Vision-Language-Action Model | A fundamental objective of manipulation policy design is to endow robots to comprehend human instructions, reason about scene cues, and execute generalized actions in dynamic environments. Recent autoregressive vision-language-action (VLA) methods inherit common-sense reasoning capabilities from vision-language models ... | [
"Jiaming Liu",
"Hao Chen",
"Pengju An",
"Zhuoyang Liu",
"Renrui Zhang",
"Chenyang Gu",
"Xiaoqi Li",
"Ziyu Guo",
"Sixiang Chen",
"Mengzhen Liu",
"Chengkai Hou",
"Mengdi Zhao",
"KC alex Zhou",
"Pheng-Ann Heng",
"Shanghang Zhang"
] | [
"cs.CV",
"cs.RO"
] | [
"Computer Science"
] | 2025-03-13T00:00:00 | https://arxiv.org/abs/2503.10631 | https://arxiv.org/pdf/2503.10631v3 | 2503.10631 | 10.48550/arXiv.2503.10631 | 175 | 5 | false | null | arXiv.org | 0.5614 |
75980a8d8130de4a10c534ddd07bc0f7769624d90c770a6dba29c7f21d33045b | [
"arxiv",
"semantic_scholar"
] | TLA: Tactile-Language-Action Model for Contact-Rich Manipulation | Significant progress has been made in vision-language models. However, language-conditioned robotic manipulation for contact-rich tasks remains underexplored, particularly in terms of tactile sensing. To address this gap, we introduce the Tactile-Language-Action (TLA) model, which effectively processes sequential tacti... | [
"Peng Hao",
"Chaofan Zhang",
"Dingzhe Li",
"Xiaoge Cao",
"Xiaoshuai Hao",
"Shaowei Cui",
"Shuo Wang"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-03-11T00:00:00 | https://arxiv.org/abs/2503.08548 | https://arxiv.org/pdf/2503.08548v1 | 2503.08548 | 10.48550/arXiv.2503.08548 | 55 | 3 | false | null | null | 0.437 |
34d0ec90cd47d06a6d9875742707a8afa98e06c7dd24c6dfe7bced873b1c38bd | [
"arxiv",
"semantic_scholar"
] | BEHAVIOR Robot Suite: Streamlining Real-World Whole-Body Manipulation for Everyday Household Activities | Real-world household tasks present significant challenges for mobile manipulation robots. An analysis of existing robotics benchmarks reveals that successful task performance hinges on three key whole-body control capabilities: bimanual coordination, stable and precise navigation, and extensive end-effector reachabilit... | [
"Yunfan Jiang",
"Ruohan Zhang",
"Josiah Wong",
"Chen Wang",
"Yanjie Ze",
"Hang Yin",
"Cem Gokmen",
"Shuran Song",
"Jiajun Wu",
"Li Fei-Fei"
] | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-03-07T00:00:00 | https://arxiv.org/abs/2503.05652 | https://arxiv.org/pdf/2503.05652v2 | 2503.05652 | 10.48550/arXiv.2503.05652 | 44 | 4 | true | null | arXiv.org | 0.4133 |
1491111b1b135070cac439e4d00011e3a85fc67a208336426016bd25a0bc5981 | [
"arxiv",
"semantic_scholar"
] | VLA Model-Expert Collaboration for Bi-directional Manipulation Learning | The emergence of vision-language-action (VLA) models has given rise to foundation models for robot manipulation. Although these models have achieved significant improvements, their generalization in multi-task manipulation remains limited. This study proposes a VLA model-expert collaboration framework that leverages a ... | [
"Tian-Yu Xiang",
"Ao-Qun Jin",
"Xiao-Hu Zhou",
"Mei-Jiang Gui",
"Xiao-Liang Xie",
"Shi-Qi Liu",
"Shuang-Yi Wang",
"Sheng-Bin Duang",
"Si-Cheng Wang",
"Zheng Lei",
"Zeng-Guang Hou"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-03-06T00:00:00 | https://arxiv.org/abs/2503.04163 | https://arxiv.org/pdf/2503.04163v1 | 2503.04163 | 10.48550/arXiv.2503.04163 | 5 | 2 | false | null | arXiv.org | 0.2386 |
46f2cac3420c5e2042b0757437a9a6154d15f613b45c70e41e5fa0fa82dae0e0 | [
"arxiv",
"semantic_scholar"
] | PD-VLA: Accelerating Vision-Language-Action Model Integrated with Action Chunking via Parallel Decoding | Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation. The performance of VLA models can be improved by integrating with action chunking, a critical technique for effective control. However, action chunking linearly scales up action dimensions in VLA models with inc... | [
"Wenxuan Song",
"Jiayi Chen",
"Pengxiang Ding",
"Han Zhao",
"Wei Zhao",
"Zhide Zhong",
"Zongyuan Ge",
"Zhijun Li",
"Donglin Wang",
"Jun Ma",
"Lujia Wang",
"Haoang Li"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-03-04T00:00:00 | https://arxiv.org/abs/2503.02310 | https://arxiv.org/pdf/2503.02310v2 | 2503.02310 | 10.1109/IROS60139.2025.11247519 | 71 | 3 | false | null | IEEE/RJS International Conference on Intelligent RObots and Systems | 0.4643 |
68f1dc6b63233d184874cf96ee0653463c0bc4a171be6a04cd4e4f239ed71218 | [
"arxiv",
"semantic_scholar"
] | Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success | Recent vision-language-action models (VLAs) build upon pretrained vision-language models and leverage diverse robot datasets to demonstrate strong task execution, language following ability, and semantic generalization. Despite these successes, VLAs struggle with novel robot setups and require fine-tuning to achieve go... | [
"Moo Jin Kim",
"Chelsea Finn",
"Percy Liang"
] | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-27T00:00:00 | https://arxiv.org/abs/2502.19645 | https://arxiv.org/pdf/2502.19645v2 | 2502.19645 | 10.48550/arXiv.2502.19645 | 564 | 131 | false | null | Robotics | 1 |
b008509aee14c9683bfd4e805dfc86ca3f08d9b57a59ceaeba2247860b9cd4d0 | [
"arxiv",
"semantic_scholar"
] | ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model | Humans possess a unified cognitive ability to perceive, comprehend, and interact with the physical world. Why can't large language models replicate this holistic understanding? Through a systematic analysis of existing training paradigms in vision-language-action models (VLA), we identify two key challenges: spurious f... | [
"Zhongyi Zhou",
"Yichen Zhu",
"Minjie Zhu",
"Junjie Wen",
"Ning Liu",
"Zhiyuan Xu",
"Weibin Meng",
"Ran Cheng",
"Yaxin Peng",
"Chaomin Shen",
"Feifei Feng"
] | [
"cs.RO",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-20T00:00:00 | https://arxiv.org/abs/2502.14420 | https://arxiv.org/pdf/2502.14420v2 | 2502.14420 | 10.48550/arXiv.2502.14420 | 118 | 7 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.5189 |
68b959e7359500def0df2a838298b1f0a4929227be7f2e91089189561032d3e5 | [
"arxiv",
"semantic_scholar"
] | VLAS: Vision-Language-Action Model With Speech Instructions For Customized Robot Manipulation | Vision-language-action models (VLAs) have become increasingly popular in robot manipulation for their end-to-end design and remarkable performance. However, existing VLAs rely heavily on vision-language models (VLMs) that only support text-based instructions, neglecting the more natural speech modality for human-robot ... | [
"Wei Zhao",
"Pengxiang Ding",
"Min Zhang",
"Zhefei Gong",
"Shuanghao Bai",
"Han Zhao",
"Donglin Wang"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-02-19T00:00:00 | https://arxiv.org/abs/2502.13508 | https://arxiv.org/pdf/2502.13508v2 | 2502.13508 | 10.48550/arXiv.2502.13508 | 55 | 0 | false | null | International Conference on Learning Representations | 0.437 |
8237f8684af39d2817ad0f5fc28188db173837c553680381e4ab7f7e667fa354 | [
"arxiv",
"semantic_scholar"
] | VLP: Vision-Language Preference Learning for Embodied Manipulation | Reward engineering is one of the key challenges in Reinforcement Learning (RL). Preference-based RL effectively addresses this issue by learning from human feedback. However, it is both time-consuming and expensive to collect human preference labels. In this paper, we propose a novel \textbf{V}ision-\textbf{L}anguage \... | [
"Runze Liu",
"Chenjia Bai",
"Jiafei Lyu",
"Shengjie Sun",
"Yali Du",
"Xiu Li"
] | [
"cs.LG",
"cs.RO"
] | [
"Computer Science"
] | 2025-02-17T00:00:00 | https://arxiv.org/abs/2502.11918 | https://arxiv.org/pdf/2502.11918v1 | 2502.11918 | 10.48550/arXiv.2502.11918 | 5 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.1945 |
5a77920aaea8ef55ea88d5e31ebd4c84647b00e85854361c35d8e3e9695c4f7b | [
"arxiv",
"semantic_scholar"
] | Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence | We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document understanding. Our model is trained on a comprehensive instruction-following dataset, including document-related tasks, such as content extrac... | [
" Granite Vision Team",
"Leonid Karlinsky",
"Assaf Arbelle",
"Abraham Daniels",
"Ahmed Nassar",
"Amit Alfassi",
"Bo Wu",
"Eli Schwartz",
"Dhiraj Joshi",
"Jovana Kondic",
"Nimrod Shabtay",
"Pengyuan Li",
"Roei Herzig",
"Shafiq Abedin",
"Shaked Perek",
"Sivan Harary",
"Udi Barzelay",
... | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2025-02-14T00:00:00 | https://arxiv.org/abs/2502.09927 | https://arxiv.org/pdf/2502.09927v1 | 2502.09927 | 10.48550/arXiv.2502.09927 | 25 | 3 | false | null | arXiv.org | 0.3537 |
6a03997790fe9e0ba16c338ac8c38fcf4365f2afe1eb6b0598cd931eefe27b67 | [
"arxiv",
"semantic_scholar"
] | EmbodiedBench: Comprehensive Benchmarking Multi-modal Large Language Models for Vision-Driven Embodied Agents | Leveraging Multi-modal Large Language Models (MLLMs) to create embodied agents offers a promising avenue for tackling real-world tasks. While language-centric embodied agents have garnered substantial attention, MLLM-based embodied agents remain underexplored due to the lack of comprehensive evaluation frameworks. To b... | [
"Rui Yang",
"Hanyang Chen",
"Junyu Zhang",
"Mark Zhao",
"Cheng Qian",
"Kangrui Wang",
"Qineng Wang",
"Teja Venkat Koripella",
"Marziyeh Movahedi",
"Manling Li",
"Heng Ji",
"Huan Zhang",
"Tong Zhang"
] | [
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2025-02-13T00:00:00 | https://arxiv.org/abs/2502.09560 | https://arxiv.org/pdf/2502.09560v3 | 2502.09560 | 10.48550/arXiv.2502.09560 | 172 | 16 | true | null | International Conference on Machine Learning | 0.6152 |
0b6b2b911c8ce7b2892800c379f3b4e56710cf649cc247143a89599158ef6c81 | [
"arxiv",
"semantic_scholar"
] | DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control | Enabling robots to perform diverse tasks across varied environments is a central challenge in robot learning. While vision-language-action (VLA) models have shown promise for generalizable robot skills, realizing their full potential requires addressing limitations in action representation and efficient training. Curre... | [
"Junjie Wen",
"Yichen Zhu",
"Jinming Li",
"Zhibin Tang",
"Chaomin Shen",
"Feifei Feng"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2025-02-09T00:00:00 | https://arxiv.org/abs/2502.05855 | https://arxiv.org/pdf/2502.05855v3 | 2502.05855 | 10.48550/arXiv.2502.05855 | 181 | 6 | false | null | arXiv.org | 0.565 |
6f0338a50619bf144d5cff870e936d510e5d7c4e0e9b01013b442b2d3c204c46 | [
"arxiv",
"semantic_scholar"
] | HAMSTER: Hierarchical Action Models For Open-World Robot Manipulation | Large foundation models have shown strong open-world generalization to complex problems in vision and language, but similar levels of generalization have yet to be achieved in robotics. One fundamental challenge is the lack of robotic data, which are typically obtained through expensive on-robot operation. A promising ... | [
"Yi Li",
"Yuquan Deng",
"Jesse Zhang",
"Joel Jang",
"Marius Memmel",
"Raymond Yu",
"Caelan Reed Garrett",
"Fabio Ramos",
"Dieter Fox",
"Anqi Li",
"Abhishek Gupta",
"Ankit Goyal"
] | [
"cs.RO",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2025-02-08T00:00:00 | https://arxiv.org/abs/2502.05485 | https://arxiv.org/pdf/2502.05485v4 | 2502.05485 | 10.48550/arXiv.2502.05485 | 115 | 7 | false | null | International Conference on Learning Representations | 0.5161 |
59f2870aba14fcc55524b07ef005fe24ea3e686736227b8d08380aad0bdfcede | [
"arxiv",
"semantic_scholar"
] | VLA-Cache: Efficient Vision-Language-Action Manipulation via Adaptive Token Caching | Vision-Language-Action (VLA) models have demonstrated strong multi-modal reasoning capabilities, enabling direct action generation from visual perception and language instructions in an end-to-end manner. However, their substantial computational cost poses a challenge for real-time robotic control, where rapid decision... | [
"Siyu Xu",
"Yunke Wang",
"Chenghao Xia",
"Dihao Zhu",
"Tao Huang",
"Chang Xu"
] | [
"cs.RO",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-04T00:00:00 | https://arxiv.org/abs/2502.02175 | https://arxiv.org/pdf/2502.02175v2 | 2502.02175 | null | 46 | 9 | false | null | null | 0.5 |
be5b806583c666e61c70012c697f20ae599e4cf7e4d4f8e71360eb3f0fe74ed3 | [
"arxiv",
"semantic_scholar"
] | Provable Ordering and Continuity in Vision-Language Pretraining for Generalizable Embodied Agents | Pre-training vision-language representations on human action videos has emerged as a promising approach to reduce reliance on large-scale expert demonstrations for training embodied agents. However, prior methods often employ time contrastive learning based on goal-reaching heuristics, progressively aligning language i... | [
"Zhizhen Zhang",
"Lei Zhu",
"Zhen Fang",
"Zi Huang",
"Yadan Luo"
] | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2025-02-03T00:00:00 | https://arxiv.org/abs/2502.01218 | https://arxiv.org/pdf/2502.01218v3 | 2502.01218 | 10.48550/arXiv.2502.01218 | 3 | 0 | false | null | arXiv.org | 0.1505 |
99d0657a49cb249fa1af0d7b03f63f599eaebf8448d4e39f4898e68cea458ecd | [
"arxiv",
"semantic_scholar"
] | UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent | Recent advancements in Vision-Language-Action (VLA) models have leveraged pre-trained Vision-Language Models (VLMs) to improve the generalization capabilities. VLMs, typically pre-trained on vision-language understanding tasks, provide rich semantic knowledge and reasoning abilities. However, prior research has shown t... | [
"Jianke Zhang",
"Yanjiang Guo",
"Yucheng Hu",
"Xiaoyu Chen",
"Xiang Zhu",
"Jianyu Chen"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2025-01-31T00:00:00 | https://arxiv.org/abs/2501.18867 | https://arxiv.org/pdf/2501.18867v3 | 2501.18867 | 10.48550/arXiv.2501.18867 | 80 | 4 | false | null | International Conference on Machine Learning | 0.4771 |
e7c48c433948404455600e88724a2be10ed307b64023034102feac1ec43d51c3 | [
"arxiv",
"semantic_scholar"
] | ImageInThat: Manipulating Images to Convey User Instructions to Robots | Foundation models are rapidly improving the capability of robots in performing everyday tasks autonomously such as meal preparation, yet robots will still need to be instructed by humans due to model performance, the difficulty of capturing user preferences, and the need for user agency. Robots can be instructed using ... | [
"Karthik Mahadevan",
"Blaine Lewis",
"Jiannan Li",
"Bilge Mutlu",
"Anthony Tang",
"Tovi Grossman"
] | [
"cs.HC",
"cs.RO"
] | [
"Computer Science"
] | 2025-01-21T00:00:00 | https://arxiv.org/abs/2503.15500 | https://arxiv.org/pdf/2503.15500v1 | 2503.15500 | 10.1109/HRI61500.2025.10974179 | 6 | 1 | false | null | IEEE/ACM International Conference on Human-Robot Interaction | 0.2113 |
494a9af5f2af2d10dce16e2f0f87865b31ba9efb26cc03aa2827a9591577ef26 | [
"arxiv",
"semantic_scholar"
] | Shake-VLA: Vision-Language-Action Model-Based System for Bimanual Robotic Manipulations and Liquid Mixing | This paper introduces Shake-VLA, a Vision-Language-Action (VLA) model-based system designed to enable bimanual robotic manipulation for automated cocktail preparation. The system integrates a vision module for detecting ingredient bottles and reading labels, a speech-to-text module for interpreting user commands, and a... | [
"Muhamamd Haris Khan",
"Selamawit Asfaw",
"Dmitrii Iarchuk",
"Miguel Altamirano Cabrera",
"Luis Moreno",
"Issatay Tokmurziyev",
"Dzmitry Tsetserukou"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2025-01-12T00:00:00 | https://arxiv.org/abs/2501.06919 | https://arxiv.org/pdf/2501.06919v1 | 2501.06919 | 10.1109/HRI61500.2025.10973961 | 13 | 0 | false | null | IEEE/ACM International Conference on Human-Robot Interaction | 0.2865 |
9c87443f3cab02a0b40ba0d175aa17f4c3f8610d121bf91fe9aabd2661cfbd2b | [
"arxiv",
"semantic_scholar"
] | "Can you be my mum?": Manipulating Social Robots in the Large Language Models Era | Recent advancements in robots powered by large language models have enhanced their conversational abilities, enabling interactions closely resembling human dialogue. However, these models introduce safety and security concerns in HRI, as they are vulnerable to manipulation that can bypass built-in safety measures. Imag... | [
"Giulio Antonio Abbo",
"Gloria Desideri",
"Tony Belpaeme",
"Micol Spitale"
] | [
"cs.HC",
"cs.CY",
"cs.RO"
] | [
"Computer Science"
] | 2025-01-08T00:00:00 | https://arxiv.org/abs/2501.04633 | https://arxiv.org/pdf/2501.04633v1 | 2501.04633 | 10.1109/HRI61500.2025.10973919 | 5 | 0 | false | null | IEEE/ACM International Conference on Human-Robot Interaction | 0.1945 |
0665f9c629af1220b76999de4f4e058e8aa38a5195e88b09da988777a425cbaa | [
"arxiv",
"semantic_scholar"
] | Agreeing to Interact in Human-Robot Interaction using Large Language Models and Vision Language Models | In human-robot interaction (HRI), the beginning of an interaction is often complex. Whether the robot should communicate with the human is dependent on several situational factors (e.g., the current human's activity, urgency of the interaction, etc.). We test whether large language models (LLM) and vision language mode... | [
"Kazuhiro Sasabuchi",
"Naoki Wake",
"Atsushi Kanehira",
"Jun Takamatsu",
"Katsushi Ikeuchi"
] | [
"cs.HC",
"cs.CL",
"cs.LG",
"cs.RO"
] | [
"Computer Science"
] | 2025-01-07T00:00:00 | https://arxiv.org/abs/2503.15491 | https://arxiv.org/pdf/2503.15491v1 | 2503.15491 | 10.1109/RO-MAN63969.2025.11217646 | 3 | 1 | false | null | IEEE International Symposium on Robot and Human Interactive Communication | 0.1505 |
3a5d4d13dee85cdfaa2058505bacb367b003faec0e6b9f8f8f5f9e5fbe51cfaf | [
"arxiv",
"semantic_scholar"
] | Embodied VideoAgent: Persistent Memory from Egocentric Videos and Embodied Sensors Enables Dynamic Scene Understanding | This paper investigates the problem of understanding dynamic 3D scenes from egocentric observations, a key challenge in robotics and embodied AI. Unlike prior studies that explored this as long-form video understanding and utilized egocentric video only, we instead propose an LLM-based agent, Embodied VideoAgent, which... | [
"Yue Fan",
"Xiaojian Ma",
"Rongpeng Su",
"Jun Guo",
"Rujie Wu",
"Xi Chen",
"Qing Li"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-12-31T00:00:00 | https://arxiv.org/abs/2501.00358 | https://arxiv.org/pdf/2501.00358v2 | 2501.00358 | 10.1109/ICCV51701.2025.00598 | 16 | 1 | false | null | IEEE International Conference on Computer Vision | 0.3076 |
7bf6dae6486f329dfeabbed62ddd21f53cb0432e5b7af1a5a68badc1f2bf9042 | [
"arxiv",
"semantic_scholar"
] | Minimalist Vision with Freeform Pixels | A minimalist vision system uses the smallest number of pixels needed to solve a vision task. While traditional cameras use a large grid of square pixels, a minimalist camera uses freeform pixels that can take on arbitrary shapes to increase their information content. We show that the hardware of a minimalist camera can... | [
"Jeremy Klotz",
"Shree K. Nayar"
] | [
"cs.CV",
"eess.IV"
] | [
"Computer Science",
"Engineering"
] | 2024-12-30T00:00:00 | https://arxiv.org/abs/2501.00142 | https://arxiv.org/pdf/2501.00142v1 | 2501.00142 | 10.1007/978-3-031-73039-9_19 | 10 | 1 | false | null | European Conference on Computer Vision | 0.2603 |
009a96de353762891ad0c862fbc0ce03b29309337a971f914cfea32180090a4c | [
"arxiv",
"semantic_scholar"
] | CoA-VLA: Improving Vision-Language-Action Models via Visual-Textual Chain-of-Affordance | Robot foundation models, particularly Vision-Language-Action (VLA) models, have garnered significant attention for their ability to enhance robot policy learning, greatly improving robot's generalization and robustness. OpenAI's recent model, O1, showcased impressive capabilities in solving complex problems by utilizin... | [
"Jinming Li",
"Yichen Zhu",
"Zhibin Tang",
"Junjie Wen",
"Minjie Zhu",
"Xiaoyu Liu",
"Chengmeng Li",
"Ran Cheng",
"Yaxin Peng",
"Yan Peng",
"Feifei Feng"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-12-29T00:00:00 | https://arxiv.org/abs/2412.20451 | https://arxiv.org/pdf/2412.20451v2 | 2412.20451 | 10.1109/ICCV51701.2025.00910 | 32 | 3 | false | null | IEEE International Conference on Computer Vision | 0.3796 |
2a0c0940fb45de0285b2286c2132b4ba9c555bfe442ff6deab84682756927777 | [
"arxiv",
"semantic_scholar"
] | What Matters in Building Vision-Language-Action Models for Generalist Robots | To utilize Foundation Vision Language Models (VLMs) for robotic tasks and motion planning, the community has proposed different methods for injecting action components into VLMs and building the Vision-Language-Action models (VLAs). In this work, we disclose the key factors that significantly influence the performance ... | [
"Xinghang Li",
"Peiyan Li",
"Long Qian",
"Minghuan Liu",
"Dong Wang",
"Jirong Liu",
"Bingyi Kang",
"Xiao Ma",
"Xinlong Wang",
"Di Guo",
"Tao Kong",
"Hanbo Zhang",
"Huaping Liu"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2024-12-18T00:00:00 | https://arxiv.org/abs/2412.14058 | https://arxiv.org/pdf/2412.14058v4 | 2412.14058 | 10.1038/s42256-025-01168-7 | 92 | 6 | true | null | Nature Machine Intelligence | 0.4921 |
4abbec799e6ff2c7bd39e973ac3be1c8d7bf8d03a5802f4cd74877e48a2ab338 | [
"arxiv",
"semantic_scholar"
] | RoboMIND: Benchmark on Multi-embodiment Intelligence Normative Data for Robot Manipulation | In this paper, we introduce RoboMIND (Multi-embodiment Intelligence Normative Data for Robot Manipulation), a dataset containing 107k demonstration trajectories across 479 diverse tasks involving 96 object classes. RoboMIND is collected through human teleoperation and encompasses comprehensive robotic-related informati... | [
"Kun Wu",
"Chengkai Hou",
"Jiaming Liu",
"Zhengping Che",
"Xiaozhu Ju",
"Zhuqin Yang",
"Meng Li",
"Yinuo Zhao",
"Zhiyuan Xu",
"Guang Yang",
"Shichao Fan",
"Xinhua Wang",
"Fei Liao",
"Zhen Zhao",
"Guangyu Li",
"Zhao Jin",
"Lecheng Wang",
"Jilei Mao",
"Ning Liu",
"Pei Ren",
"Qi... | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2024-12-18T00:00:00 | https://arxiv.org/abs/2412.13877 | https://arxiv.org/pdf/2412.13877v3 | 2412.13877 | 10.15607/RSS.2025.XXI.152 | 163 | 8 | false | null | Robotics | 0.5537 |
756631094914e2392964925e22a2f385dc2a6f322115ff89c248755e459c66f7 | [
"arxiv",
"semantic_scholar"
] | Uni-NaVid: A Video-based Vision-Language-Action Model for Unifying Embodied Navigation Tasks | A practical navigation agent must be capable of handling a wide range of interaction demands, such as following instructions, searching objects, answering questions, tracking people, and more. Existing models for embodied navigation fall short of serving as practical generalists in the real world, as they are often con... | [
"Jiazhao Zhang",
"Kunyu Wang",
"Shaoan Wang",
"Minghan Li",
"Haoran Liu",
"Songlin Wei",
"Zhongyuan Wang",
"Zhizheng Zhang",
"He Wang"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2024-12-09T00:00:00 | https://arxiv.org/abs/2412.06224 | https://arxiv.org/pdf/2412.06224v2 | 2412.06224 | 10.48550/arXiv.2412.06224 | 137 | 19 | false | null | arXiv.org | 0.6505 |
cbe239c371eda1a53e5d7eddd8707b84ec2d917a502dceb31793e2061579e58c | [
"arxiv",
"semantic_scholar"
] | NaVILA: Legged Robot Vision-Language-Action Model for Navigation | This paper proposes to solve the problem of Vision-and-Language Navigation with legged robots, which not only provides a flexible way for humans to command but also allows the robot to navigate through more challenging and cluttered scenes. However, it is non-trivial to translate human language instructions all the way... | [
"An-Chieh Cheng",
"Yandong Ji",
"Zhaojing Yang",
"Zaitian Gongye",
"Xueyan Zou",
"Jan Kautz",
"Erdem Bıyık",
"Hongxu Yin",
"Sifei Liu",
"Xiaolong Wang"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2024-12-05T00:00:00 | https://arxiv.org/abs/2412.04453 | https://arxiv.org/pdf/2412.04453v2 | 2412.04453 | 10.48550/arXiv.2412.04453 | 200 | 32 | false | null | Robotics | 0.7593 |
75d6a96686eeae4c0eeea3eb600fdb45314459f525935ea05b6aa77064c51bf7 | [
"arxiv",
"semantic_scholar"
] | CogACT: A Foundational Vision-Language-Action Model for Synergizing Cognition and Action in Robotic Manipulation | The advancement of large Vision-Language-Action (VLA) models has significantly improved robotic manipulation in terms of language-guided task execution and generalization to unseen scenarios. While existing VLAs adapted from pretrained large Vision-Language-Models (VLM) have demonstrated promising generalizability, the... | [
"Qixiu Li",
"Yaobo Liang",
"Zeyu Wang",
"Lin Luo",
"Xi Chen",
"Mozheng Liao",
"Fangyun Wei",
"Yu Deng",
"Sicheng Xu",
"Yizhong Zhang",
"Xiaofan Wang",
"Bei Liu",
"Jianlong Fu",
"Jianmin Bao",
"Dong Chen",
"Yuanchun Shi",
"Jiaolong Yang",
"Baining Guo"
] | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-11-29T00:00:00 | https://arxiv.org/abs/2411.19650 | https://arxiv.org/pdf/2411.19650v1 | 2411.19650 | 10.48550/arXiv.2411.19650 | 354 | 43 | false | null | arXiv.org | 0.8217 |
5736b2015611ed4a8c7c3ed38d141d3ad515725cd6df0b1f8775a25d0b95713f | [
"arxiv",
"semantic_scholar"
] | VARCO-VISION: Expanding Frontiers in Korean Vision-Language Models | In this paper, we introduce an open-source Korean-English vision-language model (VLM), VARCO-VISION. We incorporate a step-by-step training strategy that allows a model learn both linguistic and visual information while preserving the backbone model's knowledge. Our model demonstrates outstanding performance in diverse... | [
"Jeongho Ju",
"Daeyoung Kim",
"SunYoung Park",
"Youngjune Kim"
] | [
"cs.CV",
"cs.CL"
] | [
"Computer Science"
] | 2024-11-28T00:00:00 | https://arxiv.org/abs/2411.19103 | https://arxiv.org/pdf/2411.19103v1 | 2411.19103 | 10.48550/arXiv.2411.19103 | 5 | 0 | true | null | arXiv.org | 0.1945 |
1c23c20ec8720448df16797e5d424b0a73790dd7c361daffd331e4c77fc4fb67 | [
"arxiv",
"semantic_scholar"
] | Evaluating Vision-Language Models as Evaluators in Path Planning | Despite their promise to perform complex reasoning, large language models (LLMs) have been shown to have limited effectiveness in end-to-end planning. This has inspired an intriguing question: if these models cannot plan well, can they still contribute to the planning framework as a helpful plan evaluator? In this work... | [
"Mohamed Aghzal",
"Xiang Yue",
"Erion Plaku",
"Ziyu Yao"
] | [
"cs.CV",
"cs.CL"
] | [
"Computer Science"
] | 2024-11-27T00:00:00 | https://arxiv.org/abs/2411.18711 | https://arxiv.org/pdf/2411.18711v4 | 2411.18711 | 10.1109/CVPR52734.2025.00646 | 7 | 0 | false | null | Computer Vision and Pattern Recognition | 0.2258 |
31c6e8387d2d3f89db5f083bfd54302395f017136263b7140d5cd4df05b83af4 | [
"arxiv",
"semantic_scholar"
] | Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics | Recently in robotics, Vision-Language-Action (VLA) models have emerged as a transformative approach, enabling robots to execute complex tasks by integrating visual and linguistic inputs within an end-to-end learning framework. Despite their significant capabilities, VLA models introduce new attack surfaces. This paper ... | [
"Taowen Wang",
"Cheng Han",
"James Chenhao Liang",
"Wenhao Yang",
"Dongfang Liu",
"Luna Xinyu Zhang",
"Qifan Wang",
"Jiebo Luo",
"Ruixiang Tang"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2024-11-18T00:00:00 | https://arxiv.org/abs/2411.13587 | https://arxiv.org/pdf/2411.13587v4 | 2411.13587 | 10.1109/ICCV51701.2025.00653 | 62 | 10 | true | https://github.com/William-wAng618/roboticAttack | IEEE International Conference on Computer Vision | 0.5207 |
8df85b9fcf3438b2e95c130cc9df9530be6c7f98c9ba791f1e7b6ae451791b86 | [
"arxiv",
"semantic_scholar"
] | Benchmarking Vision, Language, & Action Models on Robotic Learning Tasks | Vision-language-action (VLA) models represent a promising direction for developing general-purpose robotic systems, demonstrating the ability to combine visual understanding, language comprehension, and action generation. However, systematic evaluation of these models across diverse robotic tasks remains limited. In th... | [
"Pranav Guruprasad",
"Harshvardhan Sikka",
"Jaewoo Song",
"Yangyue Wang",
"Paul Pu Liang"
] | [
"cs.RO",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-11-04T00:00:00 | https://arxiv.org/abs/2411.05821 | https://arxiv.org/pdf/2411.05821v2 | 2411.05821 | 10.48550/arXiv.2411.05821 | 24 | 0 | false | null | arXiv.org | 0.3495 |
b41f34969c8353e0712f0dbc237aa823247094fa0dff1e35ec6fa65b851e0a29 | [
"arxiv",
"semantic_scholar"
] | DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution | MLLMs have demonstrated remarkable comprehension and reasoning capabilities with complex language and visual data. These advances have spurred the vision of establishing a generalist robotic MLLM proficient in understanding complex human instructions and accomplishing various embodied tasks. However, developing MLLMs f... | [
"Yang Yue",
"Yulin Wang",
"Bingyi Kang",
"Yizeng Han",
"Shenzhi Wang",
"Shiji Song",
"Jiashi Feng",
"Gao Huang"
] | [
"cs.RO",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-11-04T00:00:00 | https://arxiv.org/abs/2411.02359 | https://arxiv.org/pdf/2411.02359v1 | 2411.02359 | 10.48550/arXiv.2411.02359 | 114 | 9 | true | https://github.com/yueyang130/DeeR-VLA | Neural Information Processing Systems | 0.5152 |
ad644c8e946e03dd5c29739b8f02ac8e37775a690bff03d1feea4185bb901f76 | [
"arxiv",
"semantic_scholar"
] | Task-oriented Robotic Manipulation with Vision Language Models | Vision Language Models (VLMs) play a crucial role in robotic manipulation by enabling robots to understand and interpret the visual properties of objects and their surroundings, allowing them to perform manipulation based on this multimodal understanding. Accurately understanding spatial relationships remains a non-tri... | [
"Nurhan Bulus Guran",
"Hanchi Ren",
"Jingjing Deng",
"Xianghua Xie"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-10-21T00:00:00 | https://arxiv.org/abs/2410.15863 | https://arxiv.org/pdf/2410.15863v2 | 2410.15863 | 10.48550/arXiv.2410.15863 | 5 | 0 | false | null | Advanced Concepts for Intelligent Vision Systems Conference | 0.1945 |
3abcc998cf7dee8fd1db5772dc383e8ca598723b18334fc6a4d9bcd4fd848479 | [
"arxiv",
"semantic_scholar"
] | What Am I? Evaluating the Effect of Language Fluency and Task Competency on the Perception of a Social Robot | Recent advancements in robot capabilities have enabled them to interact with people in various human-social environments (HSEs). In many of these environments, the perception of the robot often depends on its capabilities, e.g., task competency, language fluency, etc. To enable fluent human-robot interaction (HRI) in H... | [
"Shahira Ali",
"Haley N. Green",
"Tariq Iqbal"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-10-14T00:00:00 | https://arxiv.org/abs/2410.11085 | https://arxiv.org/pdf/2410.11085v1 | 2410.11085 | 10.1109/RO-MAN60168.2024.10731384 | 3 | 0 | false | null | IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), 2024 | 0.1505 |
48fa23ac5b2765e59444ed07b6da79864eae38dbb6a6c58e1e59fadc6f2bd2e9 | [
"arxiv",
"semantic_scholar"
] | Users' Perception on Appropriateness of Robotic Coaching Assistant's Disclosure Behaviors | Social robots have emerged as valuable contributors to individuals' well-being coaching. Notably, their integration into long-term human coaching trials shows particular promise, emphasizing a complementary role alongside human coaches rather than outright replacement. In this context, robots serve as supportive entiti... | [
"Atikkhan Faridkhan Nilgar",
"Manuel Dietrich",
"Kristof Van Laerhoven"
] | [
"cs.HC"
] | [
"Computer Science"
] | 2024-10-14T00:00:00 | https://arxiv.org/abs/2410.10550 | https://arxiv.org/pdf/2410.10550v1 | 2410.10550 | 10.48550/arXiv.2410.10550 | 0 | 0 | false | null | arXiv.org | 0 |
e805c58c5489646079f83aadc339a7a75c92b87ace871f6eb04c2a1669c92eb0 | [
"arxiv",
"semantic_scholar"
] | LADEV: A Language-Driven Testing and Evaluation Platform for Vision-Language-Action Models in Robotic Manipulation | Building on the advancements of Large Language Models (LLMs) and Vision Language Models (VLMs), recent research has introduced Vision-Language-Action (VLA) models as an integrated solution for robotic manipulation tasks. These models take camera images and natural language task instructions as input and directly genera... | [
"Zhijie Wang",
"Zhehua Zhou",
"Jiayang Song",
"Yuheng Huang",
"Zhan Shu",
"Lei Ma"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2024-10-07T00:00:00 | https://arxiv.org/abs/2410.05191 | https://arxiv.org/pdf/2410.05191v1 | 2410.05191 | 10.48550/arXiv.2410.05191 | 8 | 0 | false | null | arXiv.org | 0.2386 |
be752851f360dbe37b3fe97c95339df4851bf4a795924963cbdb0a5c559a3dc7 | [
"arxiv",
"semantic_scholar"
] | Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy | Generalizing language-conditioned robotic policies to new tasks remains a significant challenge, hampered by the lack of suitable simulation benchmarks. In this paper, we address this gap by introducing GemBench, a novel benchmark to assess generalization capabilities of vision-language robotic manipulation policies. G... | [
"Ricardo Garcia",
"Shizhe Chen",
"Cordelia Schmid"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2024-10-02T00:00:00 | https://arxiv.org/abs/2410.01345 | https://arxiv.org/pdf/2410.01345v2 | 2410.01345 | 10.1109/ICRA55743.2025.11127315 | 47 | 10 | false | null | IEEE International Conference on Robotics and Automation | 0.5207 |
cc124f96d31f12458d66f825c3a5b9bd1ff231cd709a68615e859e72588bf7d6 | [
"arxiv",
"semantic_scholar"
] | ScVLM: Enhancing Vision-Language Model for Safety-Critical Event Understanding | Accurately identifying, understanding and describing traffic safety-critical events (SCEs), including crashes, tire strikes, and near-crashes, is crucial for advanced driver assistance systems, automated driving systems, and traffic safety. As SCEs are rare events, most general vision-language models (VLMs) have not be... | [
"Liang Shi",
"Boyu Jiang",
"Tong Zeng",
"Feng Guo"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-10-01T00:00:00 | https://arxiv.org/abs/2410.00982 | https://arxiv.org/pdf/2410.00982v2 | 2410.00982 | 10.1109/WACVW65960.2025.00119 | 16 | 0 | true | https://github.com/datadrivenwheels/ScVLM | Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1061-1071 | 0.3076 |
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