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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
b79c998f2dc1bbdd82a9c1dfa08874d90d4310398ae1caed8960c045231d50cd | [
"arxiv",
"semantic_scholar"
] | RoboNurse-VLA: Robotic Scrub Nurse System based on Vision-Language-Action Model | In modern healthcare, the demand for autonomous robotic assistants has grown significantly, particularly in the operating room, where surgical tasks require precision and reliability. Robotic scrub nurses have emerged as a promising solution to improve efficiency and reduce human error during surgery. However, challeng... | [
"Shunlei Li",
"Jin Wang",
"Rui Dai",
"Wanyu Ma",
"Wing Yin Ng",
"Yingbai Hu",
"Zheng Li"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-09-29T00:00:00 | https://arxiv.org/abs/2409.19590 | https://arxiv.org/pdf/2409.19590v1 | 2409.19590 | 10.1109/IROS60139.2025.11246030 | 33 | 2 | false | null | IEEE/RJS International Conference on Intelligent RObots and Systems | 0.3829 |
ccb8af58bb5dcc61cd5206e617447e142987b0d723f01717d2ea925e621d7a72 | [
"arxiv",
"semantic_scholar"
] | Embodied-RAG: General Non-parametric Embodied Memory for Retrieval and Generation | There is no limit to how much a robot might explore and learn, but all of that knowledge needs to be searchable and actionable. Within language research, retrieval augmented generation (RAG) has become the workhorse of large-scale non-parametric knowledge; however, existing techniques do not directly transfer to the em... | [
"Quanting Xie",
"So Yeon Min",
"Pengliang Ji",
"Yue Yang",
"Tianyi Zhang",
"Kedi Xu",
"Aarav Bajaj",
"Ruslan Salakhutdinov",
"Matthew Johnson-Roberson",
"Yonatan Bisk"
] | [
"cs.RO",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2024-09-26T00:00:00 | https://arxiv.org/abs/2409.18313 | https://arxiv.org/pdf/2409.18313v5 | 2409.18313 | 10.48550/arXiv.2409.18313 | 53 | 2 | false | null | arXiv.org | 0.4331 |
0388695ecd8ec7af94b9af68b8f1c1d713b77387ed5bc81527d608cd4fc56d95 | [
"arxiv",
"semantic_scholar"
] | Robot See Robot Do: Imitating Articulated Object Manipulation with Monocular 4D Reconstruction | Humans can learn to manipulate new objects by simply watching others; providing robots with the ability to learn from such demonstrations would enable a natural interface specifying new behaviors. This work develops Robot See Robot Do (RSRD), a method for imitating articulated object manipulation from a single monocula... | [
"Justin Kerr",
"Chung Min Kim",
"Mingxuan Wu",
"Brent Yi",
"Qianqian Wang",
"Ken Goldberg",
"Angjoo Kanazawa"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2024-09-26T00:00:00 | https://arxiv.org/abs/2409.18121 | https://arxiv.org/pdf/2409.18121v1 | 2409.18121 | 10.48550/arXiv.2409.18121 | 61 | 8 | false | null | Conference on Robot Learning | 0.4771 |
b79bb355471d2e759af299f5e9d715948723c943b17cee87cbf30386fb53307d | [
"arxiv",
"semantic_scholar"
] | SKT: Integrating State-Aware Keypoint Trajectories with Vision-Language Models for Robotic Garment Manipulation | Automating garment manipulation poses a significant challenge for assistive robotics due to the diverse and deformable nature of garments. Traditional approaches typically require separate models for each garment type, which limits scalability and adaptability. In contrast, this paper presents a unified approach using ... | [
"Xin Li",
"Siyuan Huang",
"Qiaojun Yu",
"Zhengkai Jiang",
"Ce Hao",
"Yimeng Zhu",
"Hongsheng Li",
"Peng Gao",
"Cewu Lu"
] | [
"cs.RO",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2024-09-26T00:00:00 | https://arxiv.org/abs/2409.18082 | https://arxiv.org/pdf/2409.18082v2 | 2409.18082 | 10.1109/IROS60139.2025.11246904 | 4 | 0 | false | null | IEEE/RJS International Conference on Intelligent RObots and Systems | 0.1747 |
4a4be1087d2b1fed53fd6c390b48003a241bbe641f55854634cfc65ad904852b | [
"arxiv",
"semantic_scholar"
] | Observe Then Act: Asynchronous Active Vision-Action Model for Robotic Manipulation | In real-world scenarios, many robotic manipulation tasks are hindered by occlusions and limited fields of view, posing significant challenges for passive observation-based models that rely on fixed or wrist-mounted cameras. In this paper, we investigate the problem of robotic manipulation under limited visual observati... | [
"Guokang Wang",
"Hang Li",
"Shuyuan Zhang",
"Di Guo",
"Yanhong Liu",
"Huaping Liu"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2024-09-23T00:00:00 | https://arxiv.org/abs/2409.14891 | https://arxiv.org/pdf/2409.14891v3 | 2409.14891 | 10.1109/LRA.2025.3541334 | 14 | 0 | false | null | IEEE Robotics and Automation Letters | 0.294 |
68204e12a997fa4e66b878946d5c777fcd6eb6d4e018a440fed27392d12567f9 | [
"arxiv",
"semantic_scholar"
] | Manipulation Facing Threats: Evaluating Physical Vulnerabilities in End-to-End Vision Language Action Models | Recently, driven by advancements in Multimodal Large Language Models (MLLMs), Vision Language Action Models (VLAMs) are being proposed to achieve better performance in open-vocabulary scenarios for robotic manipulation tasks. Since manipulation tasks involve direct interaction with the physical world, ensuring robustne... | [
"Hao Cheng",
"Erjia Xiao",
"Yichi Wang",
"Chengyuan Yu",
"Mengshu Sun",
"Qiang Zhang",
"Jiahang Cao",
"Yijie Guo",
"Ning Liu",
"Kaidi Xu",
"Jize Zhang",
"Chao Shen",
"Philip Torr",
"Jindong Gu",
"Renjing Xu"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-09-20T00:00:00 | https://arxiv.org/abs/2409.13174 | https://arxiv.org/pdf/2409.13174v4 | 2409.13174 | 10.48550/arXiv.2409.13174 | 16 | 0 | false | null | arXiv.org | 0.3076 |
64f2c612cb582cda3eb445ec921bfdde6b0a80843b981049a8529fe290cf18a7 | [
"arxiv",
"semantic_scholar"
] | VLATest: Testing and Evaluating Vision-Language-Action Models for Robotic Manipulation | The rapid advancement of generative AI and multi-modal foundation models has shown significant potential in advancing robotic manipulation. Vision-language-action (VLA) models, in particular, have emerged as a promising approach for visuomotor control by leveraging large-scale vision-language data and robot demonstrati... | [
"Zhijie Wang",
"Zhehua Zhou",
"Jiayang Song",
"Yuheng Huang",
"Zhan Shu",
"Lei Ma"
] | [
"cs.SE",
"cs.RO"
] | [
"Computer Science"
] | 2024-09-19T00:00:00 | https://arxiv.org/abs/2409.12894 | https://arxiv.org/pdf/2409.12894v2 | 2409.12894 | 10.1145/3729343 | 32 | 4 | false | null | null | 0.3796 |
c61c1263974b63111a7234186fa33139e0540c3e6a0e7c16e21b1d3c60564e39 | [
"arxiv",
"semantic_scholar"
] | TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation | Vision-Language-Action (VLA) models have shown remarkable potential in visuomotor control and instruction comprehension through end-to-end learning processes. However, current VLA models face significant challenges: they are slow during inference and require extensive pre-training on large amounts of robotic data, maki... | [
"Junjie Wen",
"Yichen Zhu",
"Jinming Li",
"Minjie Zhu",
"Kun Wu",
"Zhiyuan Xu",
"Ning Liu",
"Ran Cheng",
"Chaomin Shen",
"Yaxin Peng",
"Feifei Feng",
"Jian Tang"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2024-09-19T00:00:00 | https://arxiv.org/abs/2409.12514 | https://arxiv.org/pdf/2409.12514v5 | 2409.12514 | 10.1109/LRA.2025.3544909 | 352 | 13 | false | null | IEEE Robotics and Automation Letters | 0.6369 |
2e4e3ecd950a0b6aa0f08a9f06a0b7f9c3110fb36cb78010805e944f99c756c7 | [
"arxiv",
"semantic_scholar"
] | Unforgettable Generalization in Language Models | When language models (LMs) are trained to forget (or "unlearn'') a skill, how precisely does their behavior change? We study the behavior of transformer LMs in which tasks have been forgotten via fine-tuning on randomized labels. Such LMs learn to generate near-random predictions for individual examples in the "trainin... | [
"Eric Zhang",
"Leshem Chosen",
"Jacob Andreas"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-09-03T00:00:00 | https://arxiv.org/abs/2409.02228 | https://arxiv.org/pdf/2409.02228v1 | 2409.02228 | 10.48550/arXiv.2409.02228 | 4 | 0 | false | null | arXiv.org | 0.1747 |
f40c3bba12ee83b8bc51e85b2c7a30fa21dc3d79071ab602a8170625c6ba7b9c | [
"arxiv",
"semantic_scholar"
] | A Survey of Embodied Learning for Object-Centric Robotic Manipulation | Embodied learning for object-centric robotic manipulation is a rapidly developing and challenging area in embodied AI. It is crucial for advancing next-generation intelligent robots and has garnered significant interest recently. Unlike data-driven machine learning methods, embodied learning focuses on robot learning t... | [
"Ying Zheng",
"Lei Yao",
"Yuejiao Su",
"Yi Zhang",
"Yi Wang",
"Sicheng Zhao",
"Yiyi Zhang",
"Lap-Pui Chau"
] | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-08-21T00:00:00 | https://arxiv.org/abs/2408.11537 | https://arxiv.org/pdf/2408.11537v1 | 2408.11537 | 10.1007/s11633-025-1542-8 | 41 | 0 | true | https://github.com/RayYoh/OCRM_survey | Machine Intelligence Research | 0.4058 |
a0cbb1900ca875dd216fe9a1161565f055fa4b81607564cabe0e30b60d32f148 | [
"arxiv",
"semantic_scholar"
] | Learning Instruction-Guided Manipulation Affordance via Large Models for Embodied Robotic Tasks | We study the task of language instruction-guided robotic manipulation, in which an embodied robot is supposed to manipulate the target objects based on the language instructions. In previous studies, the predicted manipulation regions of the target object typically do not change with specification from the language ins... | [
"Dayou Li",
"Chenkun Zhao",
"Shuo Yang",
"Lin Ma",
"Yibin Li",
"Wei Zhang"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-08-20T00:00:00 | https://arxiv.org/abs/2408.10658 | https://arxiv.org/pdf/2408.10658v1 | 2408.10658 | 10.1109/ICARM62033.2024.10715821 | 4 | 0 | false | null | International Conference on Advanced Robotics and Mechatronics | 0.1747 |
46a67d7ade94325e7d0ba46093ea60307d2132f66446d1620cd8579ce2557526 | [
"arxiv",
"semantic_scholar"
] | Polaris: Open-ended Interactive Robotic Manipulation via Syn2Real Visual Grounding and Large Language Models | This paper investigates the task of the open-ended interactive robotic manipulation on table-top scenarios. While recent Large Language Models (LLMs) enhance robots' comprehension of user instructions, their lack of visual grounding constrains their ability to physically interact with the environment. This is because t... | [
"Tianyu Wang",
"Haitao Lin",
"Junqiu Yu",
"Yanwei Fu"
] | [
"cs.RO",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2024-08-15T00:00:00 | https://arxiv.org/abs/2408.07975 | https://arxiv.org/pdf/2408.07975v1 | 2408.07975 | 10.1109/IROS58592.2024.10801446 | 13 | 0 | false | null | IEEE/RJS International Conference on Intelligent RObots and Systems | 0.2865 |
c6edf6f3e18e95cd8df81aff97bd0bde7352d2c9fe414cd525b26fd9823eb6bc | [
"arxiv",
"semantic_scholar"
] | BadRobot: Jailbreaking Embodied LLM Agents in the Physical World | Embodied AI represents systems where AI is integrated into physical entities. Large Language Model (LLM), which exhibits powerful language understanding abilities, has been extensively employed in embodied AI by facilitating sophisticated task planning. However, a critical safety issue remains overlooked: could these e... | [
"Hangtao Zhang",
"Chenyu Zhu",
"Xianlong Wang",
"Ziqi Zhou",
"Changgan Yin",
"Minghui Li",
"Lulu Xue",
"Yichen Wang",
"Shengshan Hu",
"Aishan Liu",
"Peijin Guo",
"Leo Yu Zhang"
] | [
"cs.CY",
"cs.AI",
"cs.RO"
] | [
"Computer Science"
] | 2024-07-16T00:00:00 | https://arxiv.org/abs/2407.20242 | https://arxiv.org/pdf/2407.20242v5 | 2407.20242 | null | 48 | 5 | true | https://github.com/Rookie143/BadRobot | International Conference on Learning Representations | 0.4225 |
b139083769800791710b6838de16b0ee382fa1d10d4e6b0764e148d87f2c3181 | [
"arxiv",
"semantic_scholar"
] | VLMPC: Vision-Language Model Predictive Control for Robotic Manipulation | Although Model Predictive Control (MPC) can effectively predict the future states of a system and thus is widely used in robotic manipulation tasks, it does not have the capability of environmental perception, leading to the failure in some complex scenarios. To address this issue, we introduce Vision-Language Model Pr... | [
"Wentao Zhao",
"Jiaming Chen",
"Ziyu Meng",
"Donghui Mao",
"Ran Song",
"Wei Zhang"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-07-13T00:00:00 | https://arxiv.org/abs/2407.09829 | https://arxiv.org/pdf/2407.09829v1 | 2407.09829 | 10.48550/arXiv.2407.09829 | 40 | 2 | true | https://github.com/PPjmchen/VLMPC} | null | 0.4032 |
0dc2194f8fef2dc52eaf4563a81b9da7f6ee79c44a36b07f80dad19773c5ed42 | [
"arxiv",
"semantic_scholar"
] | Robotic Control via Embodied Chain-of-Thought Reasoning | A key limitation of learned robot control policies is their inability to generalize outside their training data. Recent works on vision-language-action models (VLAs) have shown that the use of large, internet pre-trained vision-language models as the backbone of learned robot policies can substantially improve their ro... | [
"Michał Zawalski",
"William Chen",
"Karl Pertsch",
"Oier Mees",
"Chelsea Finn",
"Sergey Levine"
] | [
"cs.RO",
"cs.LG"
] | [
"Computer Science"
] | 2024-07-11T00:00:00 | https://arxiv.org/abs/2407.08693 | https://arxiv.org/pdf/2407.08693v3 | 2407.08693 | 10.48550/arXiv.2407.08693 | 325 | 31 | true | null | Conference on Robot Learning | 0.7526 |
f796e6904202f1935d75b4140349dcd26a950bdc0e6cc73c135626d05b678ad4 | [
"arxiv",
"semantic_scholar"
] | Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI | Embodied Artificial Intelligence (Embodied AI) is crucial for achieving Artificial General Intelligence (AGI) and serves as a foundation for various applications (e.g., intelligent mechatronics systems, smart manufacturing) that bridge cyberspace and the physical world. Recently, the emergence of Multi-modal Large Mode... | [
"Yang Liu",
"Weixing Chen",
"Yongjie Bai",
"Xiaodan Liang",
"Guanbin Li",
"Wen Gao",
"Liang Lin"
] | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MA",
"cs.RO"
] | [
"Computer Science"
] | 2024-07-09T00:00:00 | https://arxiv.org/abs/2407.06886 | https://arxiv.org/pdf/2407.06886v8 | 2407.06886 | 10.1109/TMECH.2025.3574943 | 292 | 8 | true | https://github.com/HCPLab-SYSU/Embodied_AI_Paper_List | IEEE/ASME transactions on mechatronics | 0.6167 |
34077a632bf35b851adfe68183fdfb68b7f00966f9ad9356450a9c63eed59f66 | [
"arxiv",
"semantic_scholar"
] | DaDu-Corki: Algorithm-Architecture Co-Design for Embodied AI-powered Robotic Manipulation | Embodied AI robots have the potential to fundamentally improve the way human beings live and manufacture. Continued progress in the burgeoning field of using large language models to control robots depends critically on an efficient computing substrate, and this trend is strongly evident in manipulation tasks. In parti... | [
"Yiyang Huang",
"Yuhui Hao",
"Bo Yu",
"Feng Yan",
"Yuxin Yang",
"Feng Min",
"Yinhe Han",
"Lin Ma",
"Shaoshan Liu",
"Qiang Liu",
"Yiming Gan"
] | [
"cs.AR",
"cs.RO"
] | [
"Computer Science"
] | 2024-07-05T00:00:00 | https://arxiv.org/abs/2407.04292 | https://arxiv.org/pdf/2407.04292v5 | 2407.04292 | 10.1145/3695053.3731099 | 12 | 2 | false | null | International Symposium on Computer Architecture | 0.2785 |
1b80221b63521ac0c0ab4288a7986087c156082b5c06c66739d9030c50c6569f | [
"arxiv",
"semantic_scholar"
] | LLaRA: Supercharging Robot Learning Data for Vision-Language Policy | Vision Language Models (VLMs) have recently been leveraged to generate robotic actions, forming Vision-Language-Action (VLA) models. However, directly adapting a pretrained VLM for robotic control remains challenging, particularly when constrained by a limited number of robot demonstrations. In this work, we introduce ... | [
"Xiang Li",
"Cristina Mata",
"Jongwoo Park",
"Kumara Kahatapitiya",
"Yoo Sung Jang",
"Jinghuan Shang",
"Kanchana Ranasinghe",
"Ryan Burgert",
"Mu Cai",
"Yong Jae Lee",
"Michael S. Ryoo"
] | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-06-28T00:00:00 | https://arxiv.org/abs/2406.20095 | https://arxiv.org/pdf/2406.20095v3 | 2406.20095 | 10.48550/arXiv.2406.20095 | 65 | 3 | true | https://github.com/LostXine/LLaRA | International Conference on Learning Representations | 0.4549 |
3d6598e780d9d0604571e1e897bcca420e5cacd8c49d1be502af23a7b3fa1d17 | [
"arxiv",
"semantic_scholar"
] | Manipulate-Anything: Automating Real-World Robots using Vision-Language Models | Large-scale endeavors like and widespread community efforts such as Open-X-Embodiment have contributed to growing the scale of robot demonstration data. However, there is still an opportunity to improve the quality, quantity, and diversity of robot demonstration data. Although vision-language models have been shown to ... | [
"Jiafei Duan",
"Wentao Yuan",
"Wilbert Pumacay",
"Yi Ru Wang",
"Kiana Ehsani",
"Dieter Fox",
"Ranjay Krishna"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2024-06-27T00:00:00 | https://arxiv.org/abs/2406.18915 | https://arxiv.org/pdf/2406.18915v3 | 2406.18915 | 10.48550/arXiv.2406.18915 | 103 | 4 | false | null | Conference on Robot Learning | 0.5043 |
0126de19a834f912926c4cc2197c8cf74cb925b6d69e2df5612e829f52e4e57d | [
"arxiv",
"semantic_scholar"
] | ManiWAV: Learning Robot Manipulation from In-the-Wild Audio-Visual Data | Audio signals provide rich information for the robot interaction and object properties through contact. This information can surprisingly ease the learning of contact-rich robot manipulation skills, especially when the visual information alone is ambiguous or incomplete. However, the usage of audio data in robot manipu... | [
"Zeyi Liu",
"Cheng Chi",
"Eric Cousineau",
"Naveen Kuppuswamy",
"Benjamin Burchfiel",
"Shuran Song"
] | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.SD",
"eess.AS"
] | [
"Computer Science",
"Engineering"
] | 2024-06-27T00:00:00 | https://arxiv.org/abs/2406.19464 | https://arxiv.org/pdf/2406.19464v2 | 2406.19464 | 10.48550/arXiv.2406.19464 | 64 | 9 | false | null | Conference on Robot Learning | 0.5 |
c85a2d9e156f4200d16cd7c3f3a74f1dc381ed7b70cbab3012c70857ad64c607 | [
"arxiv",
"semantic_scholar"
] | LIT: Large Language Model Driven Intention Tracking for Proactive Human-Robot Collaboration -- A Robot Sous-Chef Application | Large Language Models (LLM) and Vision Language Models (VLM) enable robots to ground natural language prompts into control actions to achieve tasks in an open world. However, when applied to a long-horizon collaborative task, this formulation results in excessive prompting for initiating or clarifying robot actions at ... | [
"Zhe Huang",
"John Pohovey",
"Ananya Yammanuru",
"Katherine Driggs-Campbell"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2024-06-19T00:00:00 | https://arxiv.org/abs/2406.13787 | https://arxiv.org/pdf/2406.13787v1 | 2406.13787 | 10.48550/arXiv.2406.13787 | 5 | 0 | false | null | arXiv.org | 0.1945 |
871ed903ffb75abf358dfe7960d8d323fcada5185fa2525b760d34c3d1f9c9b8 | [
"arxiv",
"semantic_scholar"
] | RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics | From rearranging objects on a table to putting groceries into shelves, robots must plan precise action points to perform tasks accurately and reliably. In spite of the recent adoption of vision language models (VLMs) to control robot behavior, VLMs struggle to precisely articulate robot actions using language. We intro... | [
"Wentao Yuan",
"Jiafei Duan",
"Valts Blukis",
"Wilbert Pumacay",
"Ranjay Krishna",
"Adithyavairavan Murali",
"Arsalan Mousavian",
"Dieter Fox"
] | [
"cs.RO",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2024-06-15T00:00:00 | https://arxiv.org/abs/2406.10721 | https://arxiv.org/pdf/2406.10721v1 | 2406.10721 | 10.48550/arXiv.2406.10721 | 234 | 28 | false | null | arXiv.org | 0.7312 |
5ff8ad34afdce510bee0bac49b68566484470f008ed5edb3747b680ef7b5d1d0 | [
"arxiv",
"semantic_scholar"
] | EmbSpatial-Bench: Benchmarking Spatial Understanding for Embodied Tasks with Large Vision-Language Models | The recent rapid development of Large Vision-Language Models (LVLMs) has indicated their potential for embodied tasks.However, the critical skill of spatial understanding in embodied environments has not been thoroughly evaluated, leaving the gap between current LVLMs and qualified embodied intelligence unknown. Theref... | [
"Mengfei Du",
"Binhao Wu",
"Zejun Li",
"Xuanjing Huang",
"Zhongyu Wei"
] | [
"cs.AI",
"cs.CL",
"cs.CV",
"cs.MM"
] | [
"Computer Science"
] | 2024-06-09T00:00:00 | https://arxiv.org/abs/2406.05756 | https://arxiv.org/pdf/2406.05756v1 | 2406.05756 | 10.48550/arXiv.2406.05756 | 88 | 10 | false | null | Annual Meeting of the Association for Computational Linguistics | 0.5207 |
6bad067bfc5e383ecfc4d879ab082b5696ee8d0611bd0b6ef121e029a4c15775 | [
"arxiv",
"semantic_scholar"
] | RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation | A fundamental objective in robot manipulation is to enable models to comprehend visual scenes and execute actions. Although existing Vision-Language-Action (VLA) models for robots can handle a range of basic tasks, they still face challenges in two areas: (1) insufficient reasoning ability to tackle complex tasks, and ... | [
"Jiaming Liu",
"Mengzhen Liu",
"Zhenyu Wang",
"Pengju An",
"Xiaoqi Li",
"Kaichen Zhou",
"Senqiao Yang",
"Renrui Zhang",
"Yandong Guo",
"Shanghang Zhang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-06-06T00:00:00 | https://arxiv.org/abs/2406.04339 | https://arxiv.org/pdf/2406.04339v2 | 2406.04339 | 10.52202/079017-1266 | 119 | 2 | false | null | Neural Information Processing Systems | 0.5198 |
24438856f998bb9e41c385185886b0815057df45466e046ef95a3cefb993ebde | [
"arxiv",
"semantic_scholar"
] | A Survey of Language-Based Communication in Robotics | Embodied robots which can interact with their environment and neighbours are increasingly being used as a test case to develop Artificial Intelligence. This creates a need for multimodal robot controllers that can operate across different types of information, including text. Large Language Models are able to process a... | [
"William Hunt",
"Sarvapali D. Ramchurn",
"Mohammad D. Soorati"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-06-06T00:00:00 | https://arxiv.org/abs/2406.04086 | https://arxiv.org/pdf/2406.04086v4 | 2406.04086 | 10.48550/arXiv.2406.04086 | 22 | 0 | false | null | arXiv.org | 0.3404 |
e6bac34b94df04685fbdea96c1d7006b34a6848928e37f1b30406c485b1bd1b6 | [
"arxiv",
"semantic_scholar"
] | Empowering Embodied Manipulation: A Bimanual-Mobile Robot Manipulation Dataset for Household Tasks | The advancements in embodied AI are increasingly enabling robots to tackle complex real-world tasks, such as household manipulation. However, the deployment of robots in these environments remains constrained by the lack of comprehensive bimanual-mobile robot manipulation data that can be learned. Existing datasets pre... | [
"Tianle Zhang",
"Dongjiang Li",
"Yihang Li",
"Zecui Zeng",
"Lin Zhao",
"Lei Sun",
"Yue Chen",
"Xuelong Wei",
"Yibing Zhan",
"Lusong Li",
"Xiaodong He"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-05-29T00:00:00 | https://arxiv.org/abs/2405.18860 | https://arxiv.org/pdf/2405.18860v2 | 2405.18860 | 10.48550/arXiv.2405.18860 | 24 | 2 | true | null | arXiv.org | 0.3495 |
7f343ca05b8fd22b22d82c68f5f366356748222cf2fb9c449d44c60571afede1 | [
"arxiv",
"semantic_scholar"
] | A Self-Correcting Vision-Language-Action Model for Fast and Slow System Manipulation | Recently, some studies have integrated Multimodal Large Language Models into robotic manipulation, constructing vision-language-action models (VLAs) to interpret multimodal information and predict SE(3) poses. While VLAs have shown promising progress, they may suffer from failures when faced with novel and complex task... | [
"Chenxuan Li",
"Jiaming Liu",
"Guanqun Wang",
"Xiaoqi Li",
"Sixiang Chen",
"Liang Heng",
"Chuyan Xiong",
"Jiaxin Ge",
"Renrui Zhang",
"Kaichen Zhou",
"Shanghang Zhang"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2024-05-27T00:00:00 | https://arxiv.org/abs/2405.17418 | https://arxiv.org/pdf/2405.17418v2 | 2405.17418 | null | 25 | 1 | false | null | null | 0.3537 |
377c9892c6f5e63770ec7f4e92dd98cf1a0bbf81ef447f2f55f33b52a7674a4a | [
"arxiv",
"semantic_scholar"
] | A Survey on Vision-Language-Action Models for Embodied AI | Embodied AI is widely recognized as a cornerstone of artificial general intelligence (AGI) because it involves controlling embodied agents to perform tasks in the physical world. Building on the success of large language models (LLMs) and vision-language models (VLMs), a new category of multimodal models -- referred to... | [
"Yueen Ma",
"Zixing Song",
"Yuzheng Zhuang",
"Jianye Hao",
"Irwin King"
] | [
"cs.RO",
"cs.CL",
"cs.CV"
] | [
"Computer Science",
"Medicine"
] | 2024-05-23T00:00:00 | https://arxiv.org/abs/2405.14093 | https://arxiv.org/pdf/2405.14093v8 | 2405.14093 | 10.1109/TNNLS.2025.3650584 | 276 | 15 | true | https://github.com/yueen-ma/Awesome-VLA | IEEE Transactions on Neural Networks and Learning Systems | 0.6106 |
9aefad26947979bc64f3757e8eced348217b3d3c51e7c5e85cf45ac9c220938b | [
"arxiv",
"semantic_scholar"
] | DiffGen: Robot Demonstration Generation via Differentiable Physics Simulation, Differentiable Rendering, and Vision-Language Model | Generating robot demonstrations through simulation is widely recognized as an effective way to scale up robot data. Previous work often trained reinforcement learning agents to generate expert policies, but this approach lacks sample efficiency. Recently, a line of work has attempted to generate robot demonstrations vi... | [
"Yang Jin",
"Jun Lv",
"Shuqiang Jiang",
"Cewu Lu"
] | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-05-12T00:00:00 | https://arxiv.org/abs/2405.07309 | https://arxiv.org/pdf/2405.07309v1 | 2405.07309 | 10.1109/IROS60139.2025.11247245 | 3 | 0 | false | null | IEEE/RJS International Conference on Intelligent RObots and Systems | 0.1505 |
f391699c4b034bdbb311624b65b441fbad34d7d1cfb118d25ab94b75976f3464 | [
"arxiv",
"semantic_scholar"
] | Bi-VLA: Vision-Language-Action Model-Based System for Bimanual Robotic Dexterous Manipulations | This research introduces the Bi-VLA (Vision-Language-Action) model, a novel system designed for bimanual robotic dexterous manipulation that seamlessly integrates vision for scene understanding, language comprehension for translating human instructions into executable code, and physical action generation. We evaluated ... | [
"Koffivi Fidèle Gbagbe",
"Miguel Altamirano Cabrera",
"Ali Alabbas",
"Oussama Alyunes",
"Artem Lykov",
"Dzmitry Tsetserukou"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-05-09T00:00:00 | https://arxiv.org/abs/2405.06039 | https://arxiv.org/pdf/2405.06039v2 | 2405.06039 | 10.1109/SMC54092.2024.10831380 | 43 | 1 | false | null | IEEE International Conference on Systems, Man and Cybernetics | 0.4109 |
9564f8c9615fb2ce54b456c51568ccb5c0092c5b61d12202340012862792c0b9 | [
"arxiv",
"semantic_scholar"
] | Closed Loop Interactive Embodied Reasoning for Robot Manipulation | Embodied reasoning systems integrate robotic hardware and cognitive processes to perform complex tasks, typically in response to a natural language query about a specific physical environment. This usually involves changing the belief about the scene or physically interacting and changing the scene (e.g. sort the objec... | [
"Michal Nazarczuk",
"Jan Kristof Behrens",
"Karla Stepanova",
"Matej Hoffmann",
"Krystian Mikolajczyk"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2024-04-23T00:00:00 | https://arxiv.org/abs/2404.15194 | https://arxiv.org/pdf/2404.15194v2 | 2404.15194 | 10.1109/ICRA55743.2025.11127480 | 6 | 0 | false | null | IEEE International Conference on Robotics and Automation | 0.2113 |
7ecb72e8be008288e9143bdaf65ca389de7031e0938e98bd179bf77928a0134c | [
"arxiv",
"semantic_scholar"
] | OVAL-Prompt: Open-Vocabulary Affordance Localization for Robot Manipulation through LLM Affordance-Grounding | In order for robots to interact with objects effectively, they must understand the form and function of each object they encounter. Essentially, robots need to understand which actions each object affords, and where those affordances can be acted on. Robots are ultimately expected to operate in unstructured human envir... | [
"Edmond Tong",
"Anthony Opipari",
"Stanley Lewis",
"Zhen Zeng",
"Odest Chadwicke Jenkins"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-04-17T00:00:00 | https://arxiv.org/abs/2404.11000 | https://arxiv.org/pdf/2404.11000v2 | 2404.11000 | 10.48550/arXiv.2404.11000 | 28 | 2 | false | null | arXiv.org | 0.3656 |
ddb033c8d033777ada1d83c2a23a34571cdf9b4e4f87cbbe1ac8f93b767a2485 | [
"arxiv",
"semantic_scholar"
] | Enhancing Robot Explanation Capabilities through Vision-Language Models: a Preliminary Study by Interpreting Visual Inputs for Improved Human-Robot Interaction | This paper presents an improved system based on our prior work, designed to create explanations for autonomous robot actions during Human-Robot Interaction (HRI). Previously, we developed a system that used Large Language Models (LLMs) to interpret logs and produce natural language explanations. In this study, we expan... | [
"David Sobrín-Hidalgo",
"Miguel Ángel González-Santamarta",
"Ángel Manuel Guerrero-Higueras",
"Francisco Javier Rodríguez-Lera",
"Vicente Matellán-Olivera"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2024-04-15T00:00:00 | https://arxiv.org/abs/2404.09705 | https://arxiv.org/pdf/2404.09705v1 | 2404.09705 | 10.48550/arXiv.2404.09705 | 7 | 0 | false | null | arXiv.org | 0.2258 |
7b815877700211f313e195118022b330a5fa2485fa880e913515d5c45788e7ff | [
"arxiv",
"semantic_scholar"
] | Bridging Language, Vision and Action: Multimodal VAEs in Robotic Manipulation Tasks | In this work, we focus on unsupervised vision-language-action mapping in the area of robotic manipulation. Recently, multiple approaches employing pre-trained large language and vision models have been proposed for this task. However, they are computationally demanding and require careful fine-tuning of the produced ou... | [
"Gabriela Sejnova",
"Michal Vavrecka",
"Karla Stepanova"
] | [
"cs.RO",
"cs.LG"
] | [
"Computer Science"
] | 2024-04-02T00:00:00 | https://arxiv.org/abs/2404.01932 | https://arxiv.org/pdf/2404.01932v2 | 2404.01932 | 10.1109/IROS58592.2024.10802160 | 5 | 0 | false | null | IEEE/RJS International Conference on Intelligent RObots and Systems | 0.1945 |
06ef5e94a994e47d96af471764e936f7fa0e987f8970e9f99588fe3fd52684fe | [
"arxiv",
"semantic_scholar"
] | Multi-Frame, Lightweight & Efficient Vision-Language Models for Question Answering in Autonomous Driving | Vision-Language Models (VLMs) and Multi-Modal Language models (MMLMs) have become prominent in autonomous driving research, as these models can provide interpretable textual reasoning and responses for end-to-end autonomous driving safety tasks using traffic scene images and other data modalities. However, current appr... | [
"Akshay Gopalkrishnan",
"Ross Greer",
"Mohan Trivedi"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science"
] | 2024-03-28T00:00:00 | https://arxiv.org/abs/2403.19838 | https://arxiv.org/pdf/2403.19838v2 | 2403.19838 | 10.48550/arXiv.2403.19838 | 61 | 4 | true | https://github.com/akshaygopalkr/EM-VLM4AD | arXiv.org | 0.4481 |
79e2e96fd884323e4fb0ed5426f8afc7b716b92f7a54400662b72aef932b8b45 | [
"arxiv",
"semantic_scholar"
] | Are Compressed Language Models Less Subgroup Robust? | To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. However, little is known about their robustness to minority subgroups defined by the labels and attributes of a dataset. In this paper, we investigate the effects of 18 different compression ... | [
"Leonidas Gee",
"Andrea Zugarini",
"Novi Quadrianto"
] | [
"cs.LG",
"cs.CL"
] | [
"Computer Science"
] | 2024-03-26T00:00:00 | https://arxiv.org/abs/2403.17811 | https://arxiv.org/pdf/2403.17811v1 | 2403.17811 | 10.18653/v1/2023.emnlp-main.983 | 2 | 0 | false | null | Conference on Empirical Methods in Natural Language Processing | 0.1193 |
c89073844c9b9e49110ffc5e0cfce408347463afe97b8c567caa27196c1d45e8 | [
"arxiv",
"semantic_scholar"
] | 3D-VLA: A 3D Vision-Language-Action Generative World Model | Recent vision-language-action (VLA) models rely on 2D inputs, lacking integration with the broader realm of the 3D physical world. Furthermore, they perform action prediction by learning a direct mapping from perception to action, neglecting the vast dynamics of the world and the relations between actions and dynamics.... | [
"Haoyu Zhen",
"Xiaowen Qiu",
"Peihao Chen",
"Jincheng Yang",
"Xin Yan",
"Yilun Du",
"Yining Hong",
"Chuang Gan"
] | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.RO"
] | [
"Computer Science"
] | 2024-03-14T00:00:00 | https://arxiv.org/abs/2403.09631 | https://arxiv.org/pdf/2403.09631v1 | 2403.09631 | 10.48550/arXiv.2403.09631 | 336 | 15 | false | null | International Conference on Machine Learning | 0.6319 |
7d68692d2e41544e4adc4cf82293b7b7fda1dcc7842c01e3679d5610e611e5c9 | [
"arxiv",
"semantic_scholar"
] | Vision-Language Navigation with Embodied Intelligence: A Survey | As a long-term vision in the field of artificial intelligence, the core goal of embodied intelligence is to improve the perception, understanding, and interaction capabilities of agents and the environment. Vision-language navigation (VLN), as a critical research path to achieve embodied intelligence, focuses on explor... | [
"Peng Gao",
"Peng Wang",
"Feng Gao",
"Fei Wang",
"Ruyue Yuan"
] | [
"cs.RO",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2024-02-22T00:00:00 | https://arxiv.org/abs/2402.14304 | https://arxiv.org/pdf/2402.14304v2 | 2402.14304 | 10.48550/arXiv.2402.14304 | 10 | 0 | false | null | arXiv.org | 0.2603 |
97aa69029586ee3c37de4bdabc558289466e08f5b3c14f62f8e8e5e2bb8e8d51 | [
"arxiv",
"semantic_scholar"
] | Exploring the Frontier of Vision-Language Models: A Survey of Current Methodologies and Future Directions | The advent of Large Language Models (LLMs) has significantly reshaped the trajectory of the AI revolution. Nevertheless, these LLMs exhibit a notable limitation, as they are primarily adept at processing textual information. To address this constraint, researchers have endeavored to integrate visual capabilities with L... | [
"Akash Ghosh",
"Arkadeep Acharya",
"Sriparna Saha",
"Vinija Jain",
"Aman Chadha"
] | [
"cs.CV",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2024-02-20T00:00:00 | https://arxiv.org/abs/2404.07214 | https://arxiv.org/pdf/2404.07214v4 | 2404.07214 | 10.48550/arXiv.2404.07214 | 85 | 3 | false | null | arXiv.org | 0.4836 |
6ed1a1f293d2ed238f214111046385759d502f20d03f99986605d25e487b23ae | [
"arxiv",
"semantic_scholar"
] | OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics | Remarkable progress has been made in recent years in the fields of vision, language, and robotics. We now have vision models capable of recognizing objects based on language queries, navigation systems that can effectively control mobile systems, and grasping models that can handle a wide range of objects. Despite thes... | [
"Peiqi Liu",
"Yaswanth Orru",
"Jay Vakil",
"Chris Paxton",
"Nur Muhammad Mahi Shafiullah",
"Lerrel Pinto"
] | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2024-01-22T00:00:00 | https://arxiv.org/abs/2401.12202 | https://arxiv.org/pdf/2401.12202v2 | 2401.12202 | 10.15607/RSS.2024.XX.091 | 41 | 2 | true | https://github.com/ok-robot/ok-robot | null | 0.4058 |
9f4a374dcdc5cf0b1a4f27b8c1b84d6d1f87bab92566c4129fb9ae5a66f21cbb | [
"arxiv",
"semantic_scholar"
] | QUAR-VLA: Vision-Language-Action Model for Quadruped Robots | The important manifestation of robot intelligence is the ability to naturally interact and autonomously make decisions. Traditional approaches to robot control often compartmentalize perception, planning, and decision-making, simplifying system design but limiting the synergy between different information streams. This... | [
"Pengxiang Ding",
"Han Zhao",
"Wenjie Zhang",
"Wenxuan Song",
"Min Zhang",
"Siteng Huang",
"Ningxi Yang",
"Donglin Wang"
] | [
"cs.RO",
"cs.CV"
] | [
"Computer Science"
] | 2023-12-22T00:00:00 | https://arxiv.org/abs/2312.14457 | https://arxiv.org/pdf/2312.14457v6 | 2312.14457 | 10.48550/arXiv.2312.14457 | 70 | 3 | false | null | European Conference on Computer Vision | 0.4628 |
61490e17b3ef565d759652ad8da76631642a9a16c8a929df3e696de7624b8f06 | [
"arxiv",
"semantic_scholar"
] | Bridging Language and Action: A Survey of Language-Conditioned Robot Manipulation | Language-conditioned robot manipulation is an emerging field aimed at enabling seamless communication and cooperation between humans and robotic agents by teaching robots to comprehend and execute instructions conveyed in natural language. This interdisciplinary area integrates scene understanding, language processing,... | [
"Xiangtong Yao",
"Hongkuan Zhou",
"Oier Mees",
"Yuan Meng",
"Ted Xiao",
"Yonatan Bisk",
"Jean Oh",
"Edward Johns",
"Mohit Shridhar",
"Dhruv Shah",
"Jesse Thomason",
"Kai Huang",
"Joyce Chai",
"Zhenshan Bing",
"Alois Knoll"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2023-12-17T00:00:00 | https://arxiv.org/abs/2312.10807 | https://arxiv.org/pdf/2312.10807v6 | 2312.10807 | null | 21 | 0 | false | null | null | 0.3356 |
3acb6a34d55ddf49e4b05083da943ecabae331a63a25eccec821457fee5c417c | [
"arxiv",
"semantic_scholar"
] | Exploring Large Language Models to Facilitate Variable Autonomy for Human-Robot Teaming | In a rapidly evolving digital landscape autonomous tools and robots are becoming commonplace. Recognizing the significance of this development, this paper explores the integration of Large Language Models (LLMs) like Generative pre-trained transformer (GPT) into human-robot teaming environments to facilitate variable a... | [
"Younes Lakhnati",
"Max Pascher",
"Jens Gerken"
] | [
"cs.HC",
"cs.AI",
"cs.RO"
] | [
"Computer Science",
"Medicine"
] | 2023-12-12T00:00:00 | https://arxiv.org/abs/2312.07214 | https://arxiv.org/pdf/2312.07214v3 | 2312.07214 | 10.3389/frobt.2024.1347538 | 19 | 1 | false | null | Front. Robot. AI 11:1347538 2024 | 0.3253 |
22282d73a91ae106fc996c39c2547c41dec7d04a01592ff43335b30cc92b985b | [
"arxiv",
"semantic_scholar"
] | An Embodied Generalist Agent in 3D World | Leveraging massive knowledge from large language models (LLMs), recent machine learning models show notable successes in general-purpose task solving in diverse domains such as computer vision and robotics. However, several significant challenges remain: (i) most of these models rely on 2D images yet exhibit a limited ... | [
"Jiangyong Huang",
"Silong Yong",
"Xiaojian Ma",
"Xiongkun Linghu",
"Puhao Li",
"Yan Wang",
"Qing Li",
"Song-Chun Zhu",
"Baoxiong Jia",
"Siyuan Huang"
] | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2023-11-18T00:00:00 | https://arxiv.org/abs/2311.12871 | https://arxiv.org/pdf/2311.12871v3 | 2311.12871 | 10.48550/arXiv.2311.12871 | 379 | 37 | false | null | International Conference on Machine Learning | 0.7899 |
1b2d8afa5f46d828a8e921dcf91efa31c5d80816a4c1067ddddd12a5b5b3abe9 | [
"arxiv",
"semantic_scholar"
] | CLIPSwarm: Converting text into formations of robots | We present CLIPSwarm, an algorithm to generate robot swarm formations from natural language descriptions. CLIPSwarm receives an input text and finds the position of the robots to form a shape that corresponds to the given text. To do so, we implement a variation of the Montecarlo particle filter to obtain a matching fo... | [
"Pablo Pueyo",
"Eduardo Montijano",
"Ana C. Murillo",
"Mac Schwager"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2023-11-18T00:00:00 | https://arxiv.org/abs/2311.11047 | https://arxiv.org/pdf/2311.11047v1 | 2311.11047 | 10.48550/arXiv.2311.11047 | 0 | 0 | false | null | arXiv.org | 0 |
01c0a6b6703de56fd9703eaf05437d2a2796149cb1b89ae6f545c31033c2f280 | [
"arxiv",
"semantic_scholar"
] | Vision-Language Foundation Models as Effective Robot Imitators | Recent progress in vision language foundation models has shown their ability to understand multimodal data and resolve complicated vision language tasks, including robotics manipulation. We seek a straightforward way of making use of existing vision-language models (VLMs) with simple fine-tuning on robotics data. To th... | [
"Xinghang Li",
"Minghuan Liu",
"Hanbo Zhang",
"Cunjun Yu",
"Jie Xu",
"Hongtao Wu",
"Chilam Cheang",
"Ya Jing",
"Weinan Zhang",
"Huaping Liu",
"Hang Li",
"Tao Kong"
] | [
"cs.RO",
"cs.AI",
"cs.LG"
] | [
"Computer Science"
] | 2023-11-02T00:00:00 | https://arxiv.org/abs/2311.01378 | https://arxiv.org/pdf/2311.01378v3 | 2311.01378 | 10.48550/arXiv.2311.01378 | 398 | 27 | true | null | International Conference on Learning Representations | 0.7236 |
6f92a2e641e3461abb5028d48ba479042a5174512b1949d2ab0b997f80b11b0f | [
"arxiv",
"semantic_scholar"
] | Octopus: Embodied Vision-Language Programmer from Environmental Feedback | Large vision-language models (VLMs) have achieved substantial progress in multimodal perception and reasoning. When integrated into an embodied agent, existing embodied VLM works either output detailed action sequences at the manipulation level or only provide plans at an abstract level, leaving a gap between high-leve... | [
"Jingkang Yang",
"Yuhao Dong",
"Shuai Liu",
"Bo Li",
"Ziyue Wang",
"Chencheng Jiang",
"Haoran Tan",
"Jiamu Kang",
"Yuanhan Zhang",
"Kaiyang Zhou",
"Ziwei Liu"
] | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | [
"Computer Science"
] | 2023-10-12T00:00:00 | https://arxiv.org/abs/2310.08588 | https://arxiv.org/pdf/2310.08588v2 | 2310.08588 | 10.48550/arXiv.2310.08588 | 96 | 3 | true | https://github.com/dongyh20/Octopus | European Conference on Computer Vision | 0.4967 |
ea38021f93d750577510c5104b002bb3e18127cbdfed94b96c21117698c3468d | [
"arxiv",
"semantic_scholar"
] | Towards End-to-End Embodied Decision Making via Multi-modal Large Language Model: Explorations with GPT4-Vision and Beyond | In this study, we explore the potential of Multimodal Large Language Models (MLLMs) in improving embodied decision-making processes for agents. While Large Language Models (LLMs) have been widely used due to their advanced reasoning skills and vast world knowledge, MLLMs like GPT4-Vision offer enhanced visual understan... | [
"Liang Chen",
"Yichi Zhang",
"Shuhuai Ren",
"Haozhe Zhao",
"Zefan Cai",
"Yuchi Wang",
"Peiyi Wang",
"Tianyu Liu",
"Baobao Chang"
] | [
"cs.AI",
"cs.CL",
"cs.CV",
"cs.RO"
] | [
"Computer Science"
] | 2023-10-03T00:00:00 | https://arxiv.org/abs/2310.02071 | https://arxiv.org/pdf/2310.02071v4 | 2310.02071 | 10.48550/arXiv.2310.02071 | 58 | 4 | true | https://github.com/pkunlp-icler/PCA-EVAL/ | arXiv.org | 0.4427 |
0cb30774b8e2c79fb79dd5b5629df29382102bbc0c5970ae6bf66b46da7e54d4 | [
"arxiv",
"semantic_scholar"
] | Speech-Gesture GAN: Gesture Generation for Robots and Embodied Agents | Embodied agents, in the form of virtual agents or social robots, are rapidly becoming more widespread. In human-human interactions, humans use nonverbal behaviours to convey their attitudes, feelings, and intentions. Therefore, this capability is also required for embodied agents in order to enhance the quality and eff... | [
"Carson Yu Liu",
"Gelareh Mohammadi",
"Yang Song",
"Wafa Johal"
] | [
"cs.AI",
"cs.RO"
] | [
"Computer Science"
] | 2023-09-17T00:00:00 | https://arxiv.org/abs/2309.09346 | https://arxiv.org/pdf/2309.09346v1 | 2309.09346 | 10.1109/RO-MAN57019.2023.10309493 | 6 | 0 | false | null | IEEE International Symposium on Robot and Human Interactive Communication | 0.2113 |
3085804ff1c0ddf0c935506e3416381b3c334b6840aa98ac59dc27b6bf1e15d1 | [
"arxiv",
"semantic_scholar"
] | Incremental Learning of Humanoid Robot Behavior from Natural Interaction and Large Language Models | Natural-language dialog is key for intuitive human-robot interaction. It can be used not only to express humans' intents, but also to communicate instructions for improvement if a robot does not understand a command correctly. Of great importance is to endow robots with the ability to learn from such interaction experi... | [
"Leonard Bärmann",
"Rainer Kartmann",
"Fabian Peller-Konrad",
"Jan Niehues",
"Alex Waibel",
"Tamim Asfour"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science",
"Medicine"
] | 2023-09-08T00:00:00 | https://arxiv.org/abs/2309.04316 | https://arxiv.org/pdf/2309.04316v3 | 2309.04316 | 10.3389/frobt.2024.1455375 | 47 | 3 | false | null | Frontiers in Robotics and AI, Volume 11 - 2024 | 0.4203 |
6b19ee89ced3815f7c7b9b65fcdef9aca17a5350b621c530d6f51f860c5d1feb | [
"arxiv",
"semantic_scholar"
] | Physically Grounded Vision-Language Models for Robotic Manipulation | Recent advances in vision-language models (VLMs) have led to improved performance on tasks such as visual question answering and image captioning. Consequently, these models are now well-positioned to reason about the physical world, particularly within domains such as robotic manipulation. However, current VLMs are li... | [
"Jensen Gao",
"Bidipta Sarkar",
"Fei Xia",
"Ted Xiao",
"Jiajun Wu",
"Brian Ichter",
"Anirudha Majumdar",
"Dorsa Sadigh"
] | [
"cs.RO",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2023-09-05T00:00:00 | https://arxiv.org/abs/2309.02561 | https://arxiv.org/pdf/2309.02561v4 | 2309.02561 | 10.1109/ICRA57147.2024.10610090 | 258 | 8 | false | null | IEEE International Conference on Robotics and Automation | 0.6033 |
0f1d37f2ddd135137d5bb05068a8b8a6e63d800561db0cb953129831947033d4 | [
"arxiv",
"semantic_scholar"
] | Language-Conditioned Change-point Detection to Identify Sub-Tasks in Robotics Domains | In this work, we present an approach to identify sub-tasks within a demonstrated robot trajectory using language instructions. We identify these sub-tasks using language provided during demonstrations as guidance to identify sub-segments of a longer robot trajectory. Given a sequence of natural language instructions an... | [
"Divyanshu Raj",
"Chitta Baral",
"Nakul Gopalan"
] | [
"cs.RO",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2023-09-01T00:00:00 | https://arxiv.org/abs/2309.00743 | https://arxiv.org/pdf/2309.00743v1 | 2309.00743 | 10.48550/arXiv.2309.00743 | 1 | 0 | false | null | arXiv.org | 0.0753 |
fa66eddb67cb45fe13840124cbc666e2f6cd38d5d9a5f480bf07ee1c5771ee25 | [
"arxiv",
"semantic_scholar"
] | Explaining Vision and Language through Graphs of Events in Space and Time | Artificial Intelligence makes great advances today and starts to bridge the gap between vision and language. However, we are still far from understanding, explaining and controlling explicitly the visual content from a linguistic perspective, because we still lack a common explainable representation between the two dom... | [
"Mihai Masala",
"Nicolae Cudlenco",
"Traian Rebedea",
"Marius Leordeanu"
] | [
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2023-08-29T00:00:00 | https://arxiv.org/abs/2309.08612 | https://arxiv.org/pdf/2309.08612v1 | 2309.08612 | 10.1109/ICCVW60793.2023.00302 | 6 | 0 | false | null | null | 0.2113 |
98ce03040415cbad0350de0d3f30fe5f666d6078b91849858f670d3a052b93cf | [
"arxiv",
"semantic_scholar"
] | Towards Vision-Language Mechanistic Interpretability: A Causal Tracing Tool for BLIP | Mechanistic interpretability seeks to understand the neural mechanisms that enable specific behaviors in Large Language Models (LLMs) by leveraging causality-based methods. While these approaches have identified neural circuits that copy spans of text, capture factual knowledge, and more, they remain unusable for multi... | [
"Vedant Palit",
"Rohan Pandey",
"Aryaman Arora",
"Paul Pu Liang"
] | [
"cs.CL",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2023-08-27T00:00:00 | https://arxiv.org/abs/2308.14179 | https://arxiv.org/pdf/2308.14179v1 | 2308.14179 | 10.1109/ICCVW60793.2023.00307 | 58 | 5 | true | https://github.com/vedantpalit/Towards-Vision-Language-Mechanistic-Interpretability | null | 0.4427 |
cdcff75ba2dca54a8ee337c180395f52f94b81d189c8cff7a604208ffed8d43f | [
"arxiv",
"semantic_scholar"
] | RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control | We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefit... | [
"Anthony Brohan",
"Noah Brown",
"Justice Carbajal",
"Yevgen Chebotar",
"Xi Chen",
"Krzysztof Choromanski",
"Tianli Ding",
"Danny Driess",
"Avinava Dubey",
"Chelsea Finn",
"Pete Florence",
"Chuyuan Fu",
"Montse Gonzalez Arenas",
"Keerthana Gopalakrishnan",
"Kehang Han",
"Karol Hausman",... | [
"cs.RO",
"cs.CL",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2023-07-28T00:00:00 | https://arxiv.org/abs/2307.15818 | https://arxiv.org/pdf/2307.15818v1 | 2307.15818 | 10.48550/arXiv.2307.15818 | 3,323 | 194 | false | null | Conference on Robot Learning | 1 |
a9c960337379cbc3c245d08847a562d1e30cbf11ee599d981ff4556703880988 | [
"arxiv",
"semantic_scholar"
] | VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models | Large language models (LLMs) are shown to possess a wealth of actionable knowledge that can be extracted for robot manipulation in the form of reasoning and planning. Despite the progress, most still rely on pre-defined motion primitives to carry out the physical interactions with the environment, which remains a major... | [
"Wenlong Huang",
"Chen Wang",
"Ruohan Zhang",
"Yunzhu Li",
"Jiajun Wu",
"Li Fei-Fei"
] | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2023-07-12T00:00:00 | https://arxiv.org/abs/2307.05973 | https://arxiv.org/pdf/2307.05973v2 | 2307.05973 | 10.48550/arXiv.2307.05973 | 954 | 71 | false | null | Conference on Robot Learning | 0.9287 |
efa478aeacdb375f1334c9d9cdad21004629a0194f75d279b0db6bd8e1fffec1 | [
"arxiv",
"semantic_scholar"
] | VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View | Incremental decision making in real-world environments is one of the most challenging tasks in embodied artificial intelligence. One particularly demanding scenario is Vision and Language Navigation~(VLN) which requires visual and natural language understanding as well as spatial and temporal reasoning capabilities. Th... | [
"Raphael Schumann",
"Wanrong Zhu",
"Weixi Feng",
"Tsu-Jui Fu",
"Stefan Riezler",
"William Yang Wang"
] | [
"cs.AI",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2023-07-12T00:00:00 | https://arxiv.org/abs/2307.06082 | https://arxiv.org/pdf/2307.06082v2 | 2307.06082 | 10.48550/arXiv.2307.06082 | 128 | 9 | false | null | AAAI Conference on Artificial Intelligence | 0.5276 |
8ad798c0a411de52870f601c5ed0b305a58e2357bfec97500db7bb113972634b | [
"arxiv",
"semantic_scholar"
] | Distilling Large Vision-Language Model with Out-of-Distribution Generalizability | Large vision-language models have achieved outstanding performance, but their size and computational requirements make their deployment on resource-constrained devices and time-sensitive tasks impractical. Model distillation, the process of creating smaller, faster models that maintain the performance of larger models,... | [
"Xuanlin Li",
"Yunhao Fang",
"Minghua Liu",
"Zhan Ling",
"Zhuowen Tu",
"Hao Su"
] | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2023-07-06T00:00:00 | https://arxiv.org/abs/2307.03135 | https://arxiv.org/pdf/2307.03135v3 | 2307.03135 | 10.1109/ICCV51070.2023.00236 | 50 | 1 | true | https://github.com/xuanlinli17/large_vlm_distillation_ood | IEEE International Conference on Computer Vision | 0.4269 |
6b0bc32a2726903861ba258789cf51b8f5dba7e2f7b93902ef26e4ca34d9351e | [
"arxiv",
"semantic_scholar"
] | OphGLM: Training an Ophthalmology Large Language-and-Vision Assistant based on Instructions and Dialogue | Large multimodal language models (LMMs) have achieved significant success in general domains. However, due to the significant differences between medical images and text and general web content, the performance of LMMs in medical scenarios is limited. In ophthalmology, clinical diagnosis relies on multiple modalities o... | [
"Weihao Gao",
"Zhuo Deng",
"Zhiyuan Niu",
"Fuju Rong",
"Chucheng Chen",
"Zheng Gong",
"Wenze Zhang",
"Daimin Xiao",
"Fang Li",
"Zhenjie Cao",
"Zhaoyi Ma",
"Wenbin Wei",
"Lan Ma"
] | [
"cs.CV"
] | [
"Computer Science"
] | 2023-06-21T00:00:00 | https://arxiv.org/abs/2306.12174 | https://arxiv.org/pdf/2306.12174v2 | 2306.12174 | 10.48550/arXiv.2306.12174 | 54 | 5 | true | https://github.com/ML-AILab/OphGLM | arXiv.org | 0.4351 |
0083bb45e9552f6d1c24ef04243fe91bf782f8874fe0c3c742c16a72c0be858b | [
"arxiv",
"semantic_scholar"
] | Surfer: Progressive Reasoning with World Models for Robotic Manipulation | Considering how to make the model accurately understand and follow natural language instructions and perform actions consistent with world knowledge is a key challenge in robot manipulation. This mainly includes human fuzzy instruction reasoning and the following of physical knowledge. Therefore, the embodied intellige... | [
"Pengzhen Ren",
"Kaidong Zhang",
"Hetao Zheng",
"Zixuan Li",
"Yuhang Wen",
"Fengda Zhu",
"Mas Ma",
"Xiaodan Liang"
] | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2023-06-20T00:00:00 | https://arxiv.org/abs/2306.11335 | https://arxiv.org/pdf/2306.11335v4 | 2306.11335 | null | 8 | 1 | false | null | null | 0.2386 |
a0dc3b9ba5816529af4d2adad1fc773f5c8fcb211448b8e1d8d76d9397ffc2d1 | [
"arxiv",
"semantic_scholar"
] | LVLM-eHub: A Comprehensive Evaluation Benchmark for Large Vision-Language Models | Large Vision-Language Models (LVLMs) have recently played a dominant role in multimodal vision-language learning. Despite the great success, it lacks a holistic evaluation of their efficacy. This paper presents a comprehensive evaluation of publicly available large multimodal models by building a LVLM evaluation Hub (L... | [
"Peng Xu",
"Wenqi Shao",
"Kaipeng Zhang",
"Peng Gao",
"Shuo Liu",
"Meng Lei",
"Fanqing Meng",
"Siyuan Huang",
"Yu Qiao",
"Ping Luo"
] | [
"cs.CV",
"cs.AI"
] | [
"Computer Science",
"Medicine"
] | 2023-06-15T00:00:00 | https://arxiv.org/abs/2306.09265 | https://arxiv.org/pdf/2306.09265v1 | 2306.09265 | 10.1109/TPAMI.2024.3507000 | 270 | 19 | true | https://github.com/OpenGVLab/Multi-Modality-Arena | IEEE Transactions on Pattern Analysis and Machine Intelligence | 0.6505 |
a4eb60db870a0f4b5c61c3262c3a7e02b41080539bf3e81d7ec64047facae78b | [
"arxiv",
"semantic_scholar"
] | AlphaBlock: Embodied Finetuning for Vision-Language Reasoning in Robot Manipulation | We propose a novel framework for learning high-level cognitive capabilities in robot manipulation tasks, such as making a smiley face using building blocks. These tasks often involve complex multi-step reasoning, presenting significant challenges due to the limited paired data connecting human instructions (e.g., makin... | [
"Chuhao Jin",
"Wenhui Tan",
"Jiange Yang",
"Bei Liu",
"Ruihua Song",
"Limin Wang",
"Jianlong Fu"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2023-05-30T00:00:00 | https://arxiv.org/abs/2305.18898 | https://arxiv.org/pdf/2305.18898v1 | 2305.18898 | 10.48550/arXiv.2305.18898 | 31 | 2 | false | null | arXiv.org | 0.3763 |
3a2fb406005d90555211dd1e251016ba8917275f0be021db8430608e664fb1c1 | [
"arxiv",
"semantic_scholar"
] | Learning Robot Manipulation from Cross-Morphology Demonstration | Some Learning from Demonstrations (LfD) methods handle small mismatches in the action spaces of the teacher and student. Here we address the case where the teacher's morphology is substantially different from that of the student. Our framework, Morphological Adaptation in Imitation Learning (MAIL), bridges this gap all... | [
"Gautam Salhotra",
"I-Chun Arthur Liu",
"Gaurav Sukhatme"
] | [
"cs.RO",
"cs.LG"
] | [
"Computer Science"
] | 2023-04-07T00:00:00 | https://arxiv.org/abs/2304.03833 | https://arxiv.org/pdf/2304.03833v2 | 2304.03833 | null | 15 | 1 | false | null | Conference on Robot Learning | 0.301 |
6c2dc05da615c6d77a6da48948842463df2fd5a9ddc49ad98035d8c30a9eb739 | [
"arxiv"
] | Core Challenges in Embodied Vision-Language Planning | Recent advances in the areas of Multimodal Machine Learning and Artificial Intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Robotics. Whereas many approaches and previous survey pursuits have characterised one or two of these dim... | [
"Jonathan Francis",
"Nariaki Kitamura",
"Felix Labelle",
"Xiaopeng Lu",
"Ingrid Navarro",
"Jean Oh"
] | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.HC"
] | [] | 2023-04-05T00:00:00 | https://arxiv.org/abs/2304.02738 | https://arxiv.org/pdf/2304.02738v1 | 2304.02738 | null | 0 | 0 | false | null | null | 0 |
30671526799afaaf7b86cb612e41661800ed4b18c4dd2727ac34e161e8e784d3 | [
"arxiv",
"semantic_scholar"
] | ERRA: An Embodied Representation and Reasoning Architecture for Long-horizon Language-conditioned Manipulation Tasks | This letter introduces ERRA, an embodied learning architecture that enables robots to jointly obtain three fundamental capabilities (reasoning, planning, and interaction) for solving long-horizon language-conditioned manipulation tasks. ERRA is based on tightly-coupled probabilistic inferences at two granularity levels... | [
"Chao Zhao",
"Shuai Yuan",
"Chunli Jiang",
"Junhao Cai",
"Hongyu Yu",
"Michael Yu Wang",
"Qifeng Chen"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2023-04-05T00:00:00 | https://arxiv.org/abs/2304.02251 | https://arxiv.org/pdf/2304.02251v1 | 2304.02251 | 10.1109/LRA.2023.3265893 | 19 | 0 | false | null | IEEE Robotics and Automation Letters | 0.3253 |
aa0439e57abf8572ca1976b88a4a156c43293354e626855d3a29deb449b8d808 | [
"arxiv",
"semantic_scholar"
] | Open-World Object Manipulation using Pre-trained Vision-Language Models | For robots to follow instructions from people, they must be able to connect the rich semantic information in human vocabulary, e.g. "can you get me the pink stuffed whale?" to their sensory observations and actions. This brings up a notably difficult challenge for robots: while robot learning approaches allow robots to... | [
"Austin Stone",
"Ted Xiao",
"Yao Lu",
"Keerthana Gopalakrishnan",
"Kuang-Huei Lee",
"Quan Vuong",
"Paul Wohlhart",
"Sean Kirmani",
"Brianna Zitkovich",
"Fei Xia",
"Chelsea Finn",
"Karol Hausman"
] | [
"cs.RO",
"cs.AI",
"cs.CV"
] | [
"Computer Science"
] | 2023-03-02T00:00:00 | https://arxiv.org/abs/2303.00905 | https://arxiv.org/pdf/2303.00905v2 | 2303.00905 | 10.48550/arXiv.2303.00905 | 232 | 5 | false | null | Conference on Robot Learning | 0.5918 |
bb8703da06f879bd4027ba459956dfc865d88b305c99634aeaad0f387445ca53 | [
"arxiv",
"semantic_scholar"
] | Collaborating with language models for embodied reasoning | Reasoning in a complex and ambiguous environment is a key goal for Reinforcement Learning (RL) agents. While some sophisticated RL agents can successfully solve difficult tasks, they require a large amount of training data and often struggle to generalize to new unseen environments and new tasks. On the other hand, Lar... | [
"Ishita Dasgupta",
"Christine Kaeser-Chen",
"Kenneth Marino",
"Arun Ahuja",
"Sheila Babayan",
"Felix Hill",
"Rob Fergus"
] | [
"cs.LG",
"cs.AI",
"cs.CL"
] | [
"Computer Science"
] | 2023-02-01T00:00:00 | https://arxiv.org/abs/2302.00763 | https://arxiv.org/pdf/2302.00763v1 | 2302.00763 | 10.48550/arXiv.2302.00763 | 89 | 4 | false | null | arXiv.org | 0.4886 |
0a018fb9033918d28bbb840f733d33b639d37d606661caf467f9346bc9c4314c | [
"arxiv",
"semantic_scholar"
] | "No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy | Systems for language-guided human-robot interaction must satisfy two key desiderata for broad adoption: adaptivity and learning efficiency. Unfortunately, existing instruction-following agents cannot adapt, lacking the ability to incorporate online natural language supervision, and even if they could, require hundreds ... | [
"Yuchen Cui",
"Siddharth Karamcheti",
"Raj Palleti",
"Nidhya Shivakumar",
"Percy Liang",
"Dorsa Sadigh"
] | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.HC",
"cs.LG"
] | [
"Computer Science"
] | 2023-01-06T00:00:00 | https://arxiv.org/abs/2301.02555 | https://arxiv.org/pdf/2301.02555v1 | 2301.02555 | 10.1145/3568162.3578623 | 129 | 4 | false | null | IEEE/ACM International Conference on Human-Robot Interaction | 0.5285 |
27229acfe59436b071340e12609bdfd417c44ab9dfe8c8806300b4387231c65c | [
"arxiv",
"semantic_scholar"
] | Modularity through Attention: Efficient Training and Transfer of Language-Conditioned Policies for Robot Manipulation | Language-conditioned policies allow robots to interpret and execute human instructions. Learning such policies requires a substantial investment with regards to time and compute resources. Still, the resulting controllers are highly device-specific and cannot easily be transferred to a robot with different morphology, ... | [
"Yifan Zhou",
"Shubham Sonawani",
"Mariano Phielipp",
"Simon Stepputtis",
"Heni Ben Amor"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2022-12-08T00:00:00 | https://arxiv.org/abs/2212.04573 | https://arxiv.org/pdf/2212.04573v1 | 2212.04573 | 10.48550/arXiv.2212.04573 | 28 | 0 | true | https://github.com/ir-lab/ModAttn | Conference on Robot Learning | 0.3656 |
12b1ce6e4a054411e18c24d12f74084ab3167fa3d275a2cc66753c9a8a450e64 | [
"arxiv",
"semantic_scholar"
] | Vision-Language Pre-training: Basics, Recent Advances, and Future Trends | This paper surveys vision-language pre-training (VLP) methods for multimodal intelligence that have been developed in the last few years. We group these approaches into three categories: ($i$) VLP for image-text tasks, such as image captioning, image-text retrieval, visual question answering, and visual grounding; ($ii... | [
"Zhe Gan",
"Linjie Li",
"Chunyuan Li",
"Lijuan Wang",
"Zicheng Liu",
"Jianfeng Gao"
] | [
"cs.CV",
"cs.CL"
] | [
"Computer Science"
] | 2022-10-17T00:00:00 | https://arxiv.org/abs/2210.09263 | https://arxiv.org/pdf/2210.09263v1 | 2210.09263 | 10.48550/arXiv.2210.09263 | 218 | 9 | false | null | Foundations and Trends in Computer Graphics and Vision | 0.5851 |
333f9e9fe35abdf6368af15c0a75459c8a5ea3d19137d733c7570194dd226810 | [
"arxiv",
"semantic_scholar"
] | Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledge | Medical vision-and-language pre-training (Med-VLP) has received considerable attention owing to its applicability to extracting generic vision-and-language representations from medical images and texts. Most existing methods mainly contain three elements: uni-modal encoders (i.e., a vision encoder and a language encode... | [
"Zhihong Chen",
"Guanbin Li",
"Xiang Wan"
] | [
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2022-09-15T00:00:00 | https://arxiv.org/abs/2209.07118 | https://arxiv.org/pdf/2209.07118v1 | 2209.07118 | 10.1145/3503161.3547948 | 109 | 7 | false | null | ACM Multimedia | 0.5103 |
a3f027cca3fc21684225198071d22dfc445a6fd701ad5ddc8782ac5accbe872b | [
"arxiv",
"semantic_scholar"
] | VLMbench: A Compositional Benchmark for Vision-and-Language Manipulation | Benefiting from language flexibility and compositionality, humans naturally intend to use language to command an embodied agent for complex tasks such as navigation and object manipulation. In this work, we aim to fill the blank of the last mile of embodied agents -- object manipulation by following human guidance, e.g... | [
"Kaizhi Zheng",
"Xiaotong Chen",
"Odest Chadwicke Jenkins",
"Xin Eric Wang"
] | [
"cs.RO",
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2022-06-17T00:00:00 | https://arxiv.org/abs/2206.08522 | https://arxiv.org/pdf/2206.08522v2 | 2206.08522 | 10.48550/arXiv.2206.08522 | 94 | 7 | false | null | Neural Information Processing Systems | 0.4944 |
a6ca6d9fcc9faa97585640d0c79001f117b2e4f68d2b717b9198520609343dba | [
"arxiv",
"semantic_scholar"
] | Do As I Can, Not As I Say: Grounding Language in Robotic Affordances | Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant weakness of language models is that they lack real-world experience, w... | [
"Michael Ahn",
"Anthony Brohan",
"Noah Brown",
"Yevgen Chebotar",
"Omar Cortes",
"Byron David",
"Chelsea Finn",
"Chuyuan Fu",
"Keerthana Gopalakrishnan",
"Karol Hausman",
"Alex Herzog",
"Daniel Ho",
"Jasmine Hsu",
"Julian Ibarz",
"Brian Ichter",
"Alex Irpan",
"Eric Jang",
"Rosario ... | [
"cs.RO",
"cs.CL",
"cs.LG"
] | [
"Computer Science"
] | 2022-04-04T00:00:00 | https://arxiv.org/abs/2204.01691 | https://arxiv.org/pdf/2204.01691v2 | 2204.01691 | null | 3,227 | 200 | true | null | Conference on Robot Learning | 1 |
9c88391640987418d6211b272d8e490e0a3f522fa9456e910cf538ddce130cf2 | [
"arxiv",
"semantic_scholar"
] | R3M: A Universal Visual Representation for Robot Manipulation | We study how visual representations pre-trained on diverse human video data can enable data-efficient learning of downstream robotic manipulation tasks. Concretely, we pre-train a visual representation using the Ego4D human video dataset using a combination of time-contrastive learning, video-language alignment, and an... | [
"Suraj Nair",
"Aravind Rajeswaran",
"Vikash Kumar",
"Chelsea Finn",
"Abhinav Gupta"
] | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | [
"Computer Science"
] | 2022-03-23T00:00:00 | https://arxiv.org/abs/2203.12601 | https://arxiv.org/pdf/2203.12601v3 | 2203.12601 | 10.48550/arXiv.2203.12601 | 887 | 105 | false | null | Conference on Robot Learning | 1 |
3b64e8af1a4954a1233f9644a504acc3244023dbca6a90507f8a3577a3268c7f | [
"arxiv",
"semantic_scholar"
] | VLP: A Survey on Vision-Language Pre-training | In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP) to a new era. Substantial works have shown they are beneficial for downstream uni-modal tasks and avoid training a new model from scratch. So can such pre-trained m... | [
"Feilong Chen",
"Duzhen Zhang",
"Minglun Han",
"Xiuyi Chen",
"Jing Shi",
"Shuang Xu",
"Bo Xu"
] | [
"cs.CV",
"cs.CL"
] | [
"Computer Science"
] | 2022-02-18T00:00:00 | https://arxiv.org/abs/2202.09061 | https://arxiv.org/pdf/2202.09061v4 | 2202.09061 | 10.1007/s11633-022-1369-5 | 336 | 9 | false | null | Machine Intelligence Research | 0.6319 |
91de8db4d075ee748f03df8407735ac33125da2b43f8e9dfa7f311502d15e442 | [
"arxiv",
"semantic_scholar"
] | Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents | Can world knowledge learned by large language models (LLMs) be used to act in interactive environments? In this paper, we investigate the possibility of grounding high-level tasks, expressed in natural language (e.g. "make breakfast"), to a chosen set of actionable steps (e.g. "open fridge"). While prior work focused o... | [
"Wenlong Huang",
"Pieter Abbeel",
"Deepak Pathak",
"Igor Mordatch"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.RO"
] | [
"Computer Science"
] | 2022-01-18T00:00:00 | https://arxiv.org/abs/2201.07207 | https://arxiv.org/pdf/2201.07207v2 | 2201.07207 | null | 1,599 | 98 | false | null | International Conference on Machine Learning | 0.9978 |
34e56410c2bbb6653b0bccc4e52e18318b25507b686794b28b25df9941d81889 | [
"arxiv",
"semantic_scholar"
] | CLIP-TD: CLIP Targeted Distillation for Vision-Language Tasks | Contrastive language-image pretraining (CLIP) links vision and language modalities into a unified embedding space, yielding the tremendous potential for vision-language (VL) tasks. While early concurrent works have begun to study this potential on a subset of tasks, important questions remain: 1) What is the benefit of... | [
"Zhecan Wang",
"Noel Codella",
"Yen-Chun Chen",
"Luowei Zhou",
"Jianwei Yang",
"Xiyang Dai",
"Bin Xiao",
"Haoxuan You",
"Shih-Fu Chang",
"Lu Yuan"
] | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.MM"
] | [
"Computer Science"
] | 2022-01-15T00:00:00 | https://arxiv.org/abs/2201.05729 | https://arxiv.org/pdf/2201.05729v3 | 2201.05729 | null | 46 | 2 | false | null | arXiv.org | 0.418 |
73b788ffc5defaed267d56b904f3bf9bb8beda1b30719ef436cf4e7ee4060af5 | [
"arxiv",
"semantic_scholar"
] | Unwinding Rotations Improves User Comfort with Immersive Telepresence Robots | We propose unwinding the rotations experienced by the user of an immersive telepresence robot to improve comfort and reduce VR sickness of the user. By immersive telepresence we refer to a situation where a 360\textdegree~camera on top of a mobile robot is streaming video and audio into a head-mounted display worn by a... | [
"Markku Suomalainen",
"Basak Sakcak",
"Adhi Widagdo",
"Juho Kalliokoski",
"Katherine J. Mimnaugh",
"Alexis P. Chambers",
"Timo Ojala",
"Steven M. LaValle"
] | [
"cs.RO",
"cs.HC",
"cs.MM"
] | [
"Computer Science"
] | 2022-01-07T00:00:00 | https://arxiv.org/abs/2201.02392 | https://arxiv.org/pdf/2201.02392v1 | 2201.02392 | 10.5555/3523760.3523828 | 11 | 1 | false | null | IEEE/ACM International Conference on Human-Robot Interaction | 0.2698 |
bebd2f0d479e5c2316c2e0b1a8876e95d77ed65e1fe983d163428ebbd95be408 | [
"arxiv",
"semantic_scholar"
] | DASH: Modularized Human Manipulation Simulation with Vision and Language for Embodied AI | Creating virtual humans with embodied, human-like perceptual and actuation constraints has the promise to provide an integrated simulation platform for many scientific and engineering applications. We present Dynamic and Autonomous Simulated Human (DASH), an embodied virtual human that, given natural language commands,... | [
"Yifeng Jiang",
"Michelle Guo",
"Jiangshan Li",
"Ioannis Exarchos",
"Jiajun Wu",
"C. Karen Liu"
] | [
"cs.GR",
"cs.AI",
"cs.RO"
] | [
"Computer Science"
] | 2021-08-28T00:00:00 | https://arxiv.org/abs/2108.12536 | https://arxiv.org/pdf/2108.12536v1 | 2108.12536 | 10.1145/3475946.3480950 | 3 | 0 | false | null | Symposium on Computer Animation | 0.1505 |
7bf20cda32fc2bfc77d92c894e91f2fec8d015f8c0f0466247e3cf0f57640079 | [
"arxiv",
"semantic_scholar"
] | Core Challenges in Embodied Vision-Language Planning | Recent advances in the areas of multimodal machine learning and artificial intelligence (AI) have led to the development of challenging tasks at the intersection of Computer Vision, Natural Language Processing, and Embodied AI. Whereas many approaches and previous survey pursuits have characterised one or two of these ... | [
"Jonathan Francis",
"Nariaki Kitamura",
"Felix Labelle",
"Xiaopeng Lu",
"Ingrid Navarro",
"Jean Oh"
] | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.RO"
] | [
"Computer Science"
] | 2021-06-26T00:00:00 | https://arxiv.org/abs/2106.13948 | https://arxiv.org/pdf/2106.13948v4 | 2106.13948 | 10.1613/jair.1.13646 | 63 | 0 | false | null | Journal of Artificial Intelligence Research | 0.4515 |
0e91343bb06ca1d05447ecf8e74998365abcffd13c020489def1f420ef0eec60 | [
"arxiv",
"semantic_scholar"
] | Creative Action at a Distance: A Conceptual Framework for Embodied Performance With Robotic Actors | Acting, stand-up and dancing are creative, embodied performances that nonetheless follow a script. Unless experimental or improvised, the performers draw their movements from much the same stock of embodied schemas. A slavish following of the script leaves no room for creativity, but active interpretation of the script... | [
"Philipp Wicke",
"Tony Veale"
] | [
"cs.RO"
] | [
"Computer Science",
"Medicine"
] | 2021-04-30T00:00:00 | https://arxiv.org/abs/2104.14801 | https://arxiv.org/pdf/2104.14801v1 | 2104.14801 | 10.3389/frobt.2021.662182 | 7 | 1 | false | null | Frontiers in Robotics and AI | 0.2258 |
4abf11fcee501eb02db00641f8eeb00f2aefb5730dbb21a9c0a5496f0e07cb23 | [
"arxiv",
"semantic_scholar"
] | The Road to Know-Where: An Object-and-Room Informed Sequential BERT for Indoor Vision-Language Navigation | Vision-and-Language Navigation (VLN) requires an agent to find a path to a remote location on the basis of natural-language instructions and a set of photo-realistic panoramas. Most existing methods take the words in the instructions and the discrete views of each panorama as the minimal unit of encoding. However, this... | [
"Yuankai Qi",
"Zizheng Pan",
"Yicong Hong",
"Ming-Hsuan Yang",
"Anton van den Hengel",
"Qi Wu"
] | [
"cs.CL",
"cs.CV"
] | [
"Computer Science"
] | 2021-04-09T00:00:00 | https://arxiv.org/abs/2104.04167 | https://arxiv.org/pdf/2104.04167v2 | 2104.04167 | 10.1109/ICCV48922.2021.00168 | 89 | 1 | true | https://github.com/YuankaiQi/ORIST | IEEE International Conference on Computer Vision | 0.4886 |
713e4981870194d52d0f344f82567305f357f1002c62644375b855d22434c814 | [
"arxiv",
"semantic_scholar"
] | End-User Programming of Low- and High-Level Actions for Robotic Task Planning | Programming robots for general purpose applications is extremely challenging due to the great diversity of end-user tasks ranging from manufacturing environments to personal homes. Recent work has focused on enabling end-users to program robots using Programming by Demonstration. However, teaching robots new actions fr... | [
"Ying Siu Liang",
"Damien Pellier",
"Humbert Fiorino",
"Sylvie Pesty"
] | [
"cs.RO"
] | [
"Computer Science"
] | 2021-03-26T00:00:00 | https://arxiv.org/abs/2103.14342 | https://arxiv.org/pdf/2103.14342v1 | 2103.14342 | 10.1109/RO-MAN46459.2019.8956327 | 16 | 1 | false | null | IEEE International Symposium on Robot and Human Interactive Communication | 0.3076 |
2f5a81daf25d4abe5d197ed3bbd1ce5768e210205adf9fb4fdc8b916c45efe6b | [
"arxiv",
"semantic_scholar"
] | Grasp and Motion Planning for Dexterous Manipulation for the Real Robot Challenge | This report describes our winning submission to the Real Robot Challenge (https://real-robot-challenge.com/). The Real Robot Challenge is a three-phase dexterous manipulation competition that involves manipulating various rectangular objects with the TriFinger Platform. Our approach combines motion planning with severa... | [
"Takuma Yoneda",
"Charles Schaff",
"Takahiro Maeda",
"Matthew Walter"
] | [
"cs.RO",
"cs.AI"
] | [
"Computer Science"
] | 2021-01-08T00:00:00 | https://arxiv.org/abs/2101.02842 | https://arxiv.org/pdf/2101.02842v1 | 2101.02842 | null | 14 | 0 | true | https://github.com/ripl-ttic/real-robot-challenge | arXiv.org | 0.294 |
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