Embodied large language models enable robots to complete complex tasks in unpredictable environments

Segment Anything

Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., et al. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00371 · Cited by 11,953 · see citing works

US Environmental Protection Agency

Liu, X. (1998) · Choice Reviews Online · doi:10.5860/choice.35-4455 · Cited by 5,887 · see citing works

Six views of embodied cognition

Wilson, M. (2002) · Psychonomic Bulletin & Review · doi:10.3758/bf03196322 · Cited by 4,782 · see citing works

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-T., Rocktäschel, T., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.11401 · Cited by 3,101 · see citing works

Zero-shot text-to-image generation

Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., & Sutskever, I. (2021) · arXiv · doi:10.48550/arxiv.2102.12092 · Cited by 2,417 · see citing works

Grounding DINO: Marrying DINO with Grounded Pre-training for Open-Set Object Detection

Liu, S., Zeng, Z., Ren, T., Li, F., Zhang, H., Yang, J., Jiang, Q., Li, C., Yang, J., Su, H., et al. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72970-6_3 · Cited by 2,074 · see citing works

The grand challenges of <i>Science Robotics</i>

Yang, G.-Z., Bellingham, J., Dupont, P. E., Fischer, P., Floridi, L., Full, R., Jacobstein, N., Kumar, V., McNutt, M., Merrifield, R., et al. (2018) · Science Robotics · doi:10.1126/scirobotics.aar7650 · Cited by 1,177 · see citing works

Trends and challenges in robot manipulation

Billard, A., & Kragić, D. (2019) · Science · doi:10.1126/science.aat8414 · Cited by 1,007 · see citing works

A Review on Large Language Models: Architectures, Applications, Taxonomies, Open Issues and Challenges

(2024) · doi:10.1109/access.2024.3365742 · Cited by 934 · see citing works

An embodied cognitive science?

Clark, A. (1999) · Trends in Cognitive Sciences · doi:10.1016/s1364-6613(99)01361-3 · Cited by 781 · see citing works

Code as Policies: Language Model Programs for Embodied Control

Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., & Zeng, A. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10160591 · Cited by 767 · see citing works

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

Ahn, M., Brohan, A., Brown, N., Chebotar, Y., Cortes, O., David, B., Finn, C., Fu, C., Gopalakrishnan, K., Hausman, K., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.01691 · Cited by 624 · see citing works

ProgPrompt: Generating Situated Robot Task Plans using Large Language Models

Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., & Garg, A. (2023) · 2023 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48891.2023.10161317 · Cited by 613 · see citing works

PaLM-E: An Embodied Multimodal Language Model

Driess, D., Xia, F., Sajjadi, M. S. M., Lynch, C., Chowdhery, A., Ichter, B., Wahid, A., Tompson, J., Vuong, Q., Yu, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.03378 · Cited by 591 · see citing works

ChatGPT for Robotics: Design Principles and Model Abilities

Vemprala, S. H., Bonatti, R., Bucker, A., & Kapoor, A. (2024) · IEEE Access · doi:10.1109/access.2024.3387941 · Cited by 570 · see citing works

An integrated GPU power and performance model

Hong, S., & Kim, H. (2010) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/1815961.1815998 · Cited by 486 · see citing works

LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models

Song, C. H., Sadler, B. M., Wu, J., Chao, W.-L., Washington, C., & Su, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00280 · Cited by 435 · see citing works

Towards Reasoning in Large Language Models: A Survey

Huang, J., & Chang, K. C.-C. (2023) · Findings of the Association for Computational Linguistics: ACL 2023 · doi:10.18653/v1/2023.findings-acl.67 · Cited by 428 · see citing works

Managing extreme AI risks amid rapid progress

Bengio, Y., Hinton, G., Yao, A., Song, D., Abbeel, P., Darrell, T., Harari, Y. N., Zhang, Y.-Q., Xue, L., Shalev-Shwartz, S., et al. (2024) · Science · doi:10.1126/science.adn0117 · Cited by 349 · see citing works

Smart industrial robot control trends, challenges and opportunities within manufacturing

Arents, J., & Greitans, M. (2022) · Applied Sciences · doi:10.3390/app12020937 · Cited by 338 · see citing works

Inner Monologue: Embodied Reasoning through Planning with Language Models

Huang, W., Xia, F., Xiao, T., Chan, H., Liang, J., Florence, P., Zeng, A., Tompson, J., Mordatch, I., Chebotar, Y., et al. (2022) · arXiv · doi:10.48550/arxiv.2207.05608 · Cited by 287 · see citing works

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

Brohan, A., Brown, N., Carbajal, J., Chebotar, Y., Chen, X., Choromanski, K., Ding, T., Driess, D., Dubey, A., Finn, C., et al. (2023) · arXiv · doi:10.48550/arxiv.2307.15818 · Cited by 276 · see citing works

Large language models for human–robot interaction: A review

Zhang, C., Chen, J., Li, J., Peng, Y., & Mao, Z. (2023) · Biomimetic Intelligence and Robotics · doi:10.1016/j.birob.2023.100131 · Cited by 269 · see citing works

Dynamic movement primitives in robotics: A tutorial survey

Saveriano, M., Abu-Dakka, F. J., Kramberger, A., & Peternel, L. (2023) · The International Journal of Robotics Research · doi:10.1177/02783649231201196 · Cited by 267 · see citing works

CLIPort: What and Where Pathways for Robotic Manipulation

Shridhar, M., Manuelli, L., & Fox, D. (2021) · arXiv · doi:10.48550/arxiv.2109.12098 · Cited by 223 · see citing works

Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language

Zeng, A., Attarian, M., Ichter, B., Choromanski, K., Wong, A., Welker, S., Tombari, F., Purohit, A., Ryoo, M., Sindhwani, V., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.00598 · Cited by 205 · see citing works

A Survey of Zero-shot Generalisation in Deep Reinforcement Learning

Kirk, R., Zhang, A., Grefenstette, E., & Rocktäschel, T. (2023) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.14174 · Cited by 193 · see citing works

Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks

Nikolaidis, S., Ramakrishnan, R., Gu, K., & Shah, J. (2015) · Cited by 189 · see citing works

On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward

Choi, H., Crump, C., Duriez, C., Elmquist, A., Hager, G., Han, D., Hearl, F., Hodgins, J., Jain, A., Leve, F., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1907856118 · Cited by 162 · see citing works

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

Khazatsky, A., Pertsch, K., Nair, S., Balakrishna, A., Dasari, S., Karamcheti, S., Nasiriany, S., Srirama, M., Chen, L., Ellis, K., et al. (2024) · Robotics: Science and Systems XX · doi:10.15607/rss.2024.xx.120 · Cited by 159 · see citing works

Movement templates for learning of hitting and batting

Kober, J., Mülling, K., Krömer, O., Lampert, C. H., Schölkopf, B., & Peters, J. (2010) · 2010 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2010.5509672 · Cited by 152 · see citing works

Task and Motion Planning with Large Language Models for Object Rearrangement

Ding, Y. F., Zhang, X., Paxton, C., & Zhang, S. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10342169 · Cited by 144 · see citing works

Toward next-generation learned robot manipulation

Cui, J., & Trinkle, J. (2021) · Science Robotics · doi:10.1126/scirobotics.abd9461 · Cited by 116 · see citing works

Efficient Model Learning from Joint-Action Demonstrations for Human-Robot Collaborative Tasks

Nikolaidis, S., Ramakrishnan, R., Gu, K., & Shah, J. (2015) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/2696454.2696455 · Cited by 98 · see citing works

Skills for physical artificial intelligence

Miriyev, A., & Kovač, M. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00258-y · Cited by 94 · see citing works

Concept2Robot: Learning manipulation concepts from instructions and human demonstrations

Shao, L., Migimatsu, T., Zhang, Q., Yang, K., & Bohg, J. (2021) · The International Journal of Robotics Research · doi:10.1177/02783649211046285 · Cited by 81 · see citing works

VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models

Huang, W., Wang, C., Zhang, R., Li, Y., Wu, J., & Fei-Fei, L. (2023) · arXiv · doi:10.48550/arxiv.2307.05973 · Cited by 81 · see citing works

What matters in language conditioned robotic imitation learning over unstructured data

Mees, O., Hermann, L., & Burgard, W. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3196123 · Cited by 77 · see citing works

Grounding Language with Visual Affordances over Unstructured Data

Mees, O., Diaz, J. L. B., & Burgard, W. (2023) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra48891.2023.10160396 · Cited by 71 · see citing works

Force-based robot learning of pouring skills using parametric hidden Markov models

Rozo, L. D., Jiménez, P., & Carme Torras, A. (2013) · International Workshop on Robot Motion and Control · doi:10.1109/romoco.2013.6614613 · Cited by 71 · see citing works

How can LLMs transform the robotic design process?

Stella, F., Santina, C. D., & Hughes, J. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00669-7 · Cited by 69 · see citing works

Hierarchical Diffusion Policy for Kinematics-Aware Multi-Task Robotic Manipulation

Ma, X., Patidar, S., Haughton, I., & James, S. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.01712 · Cited by 61 · see citing works

No, to the Right

Cui, Y., Karamcheti, S., Palleti, R., Shivakumar, N., Liang, P., & Sadigh, D. (2023) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3568162.3578623 · Cited by 58 · see citing works

LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding

Li, G., Jampani, V., Sun, D., & Sevilla-Lara, L. (2023) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52729.2023.01051 · Cited by 44 · see citing works

Embodied inference and spatial cognition

Friston, K. J. (2012) · Cognitive Processing · doi:10.1007/s10339-012-0519-z · Cited by 41 · see citing works

Explainable Hierarchical Imitation Learning for Robotic Drink Pouring

Zhang, D., Li, Q., Zheng, Y., Wei, L., Zhang, D., & Zhang, Z. (2021) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2021.3138280 · Cited by 38 · see citing works

Validating Robotics Simulators on Real-World Impacts

Acosta, B., Yang, W., & Posa, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3174367 · Cited by 36 · see citing works

Toward Grounded Commonsense Reasoning

Kwon, M., Hu, H., Myers, V., Karamcheti, S., Dragan, A. D., & Sadigh, D. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611218 · Cited by 32 · see citing works

Robot gaining accurate pouring skills through self-supervised learning and generalization

Huang, Y., Wilches, J., & Sun, Y. (2020) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2020.103692 · Cited by 31 · see citing works

DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models

Palo, N. D., & Johns, E. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610923 · Cited by 30 · see citing works

Analysing deep reinforcement learning agents trained with domain randomisation

Dai, T., Arulkumaran, K., Gerbert, T., Tukra, S., Behbahani, F., & Bharath, A. A. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.005 · Cited by 27 · see citing works

One-Shot Open Affordance Learning with Foundation Models

Li, G., Sun, D., Sevilla-Lara, L., & Jampani, V. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00298 · Cited by 20 · see citing works

Nonprehensile Planar Manipulation through Reinforcement Learning with Multimodal Categorical Exploration

Ferrandis, J. D. A., Moura, J., & Vijayakumar, S. (2023) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros55552.2023.10341629 · Cited by 16 · see citing works

Intelligence research should not be held back by its past

(2017) · Nature · doi:10.1038/nature.2017.22021 · Cited by 9 · see citing works

Preference-Conditioned Language-Guided Abstraction

Peng, A., Bobu, A., Li, B. Z., Sumers, T. R., Sucholutsky, I., Kumar, N., Griffiths, T. L., & Shah, J. (2024) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3610977.3634930 · Cited by 6 · see citing works

Online Estimation of Articulated Objects with Factor Graphs using Vision and Proprioceptive Sensing

Buchanan, R., Röfer, A., Moura, J., Valada, A., & Vijayakumar, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610590 · Cited by 5 · see citing works

A behavioural transformer for effective collaboration between a robot and a non-stationary human

Mon-Williams, R., Stouraitis, T., & Vijayakumar, S. (2023) · IEEE RO-MAN · doi:10.1109/ro-man57019.2023.10309643 · Cited by 3 · see citing works

CausalSim: A Causal Framework for Unbiased Trace-Driven SimulationPreprint

Alomar, A., Hamadanian, P., Nasr-Esfahany, A., Agarwal, A., Alizadeh, M., & Devavrat Shah, B. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2201.01811 · Cited by 3 · see citing works

ruaridhmon/ELLMER: v1.0.0: Initial Release

ruaridhmon (2024) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.14483538 · Cited by 1 · see citing works

Metamaterial robotics

Zheng, X., Jiang, Y., Mete, M., Li, J., Watanabe, I., Yamada, T., & Paik, J. K. (2025) · Science Robotics · doi:10.1126/scirobotics.adx1519 · Cited by 37 · see citing works

Big Data and AI‐Powered Modeling: A Pathway to Sustainable Precision Animal Nutrition

Zhang, S., Lai, C., Zhao, J.-B., & Wang, J.-J. (2025) · Advancement of science · doi:10.1002/advs.202507564 · Cited by 20 · see citing works

GNN-LLM hybrid cognitive architectures for generative task adaptation in multi-human multi-robot collaborative disassembly

Tong, X., Li, K., & Bao, J. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103169 · Cited by 14 · see citing works

Agentic AI With Orchestrator-Agent Trust: A Modular Visual Classification Framework With Trust-Aware Orchestration and RAG-Based Reasoning

Roumeliotis, K. I., Sapkota, R., Karkee, M., & Tselikas, N. D. (2026) · IEEE Access · doi:10.1109/access.2026.3662282 · Cited by 13 · see citing works

Intelligent generation of 3D disassembly processes driven by multimodal large language models

Zhao, Y., Chen, L., Yu, H., & Zheng, Y. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.04.032 · Cited by 13 · see citing works

Generative artificial intelligence in manufacturing: applications, case studies, and future directions for next-generation intelligent production systems

Shahin, M., Hosseinzadeh, A., & Chen, F. F. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-16667-5 · Cited by 14 · see citing works

Evaluating a Hybrid LLM Q-Learning/DQN Framework for Adaptive Obstacle Avoidance in Embedded Robotics

Farkh, R., Oudinet, G., & Deleruyelle, T. (2025) · AI · doi:10.3390/ai6060115 · Cited by 12 · see citing works

Intelligent bionics: Concepts, research and applications

Huang, J., Chang, Z., Qi, J., Jakobi, T., Zhang, Z., & Ren, L. (2025) · Advanced bionics. · doi:10.1016/j.abs.2025.07.002 · Cited by 11 · see citing works

Orchestrating mechanics, perception and control: Enabling embodied intelligence in humanoid robots

Huang, J., Gao, J., & Yu, Z. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104363 · Cited by 14 · see citing works

LLM-driven agent for speech-enabled control of industrial robots: A case study in snow-crab quality inspection

Kadri, I., Selouani, S., Ghribi, M., Ghali, R., & Mekhoukh, S. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.106660 · Cited by 9 · see citing works

Bridging language and action: A survey of language-conditioned robot manipulation

Yao, X., Zhou, H., Mees, O., Meng, Y., Xiao, T. G., Bisk, Y., Oh, J., Johns, E., Shridhar, M., Shah, D., et al. (2026) · The International Journal of Robotics Research · doi:10.1177/02783649261468360 · Cited by 23 · see citing works

Exploring fruit mechanical injury mechanisms: a review of numerical simulation techniques in the whole supply chain

Gao, S., Huang, X., Li, Z., Zhang, X., Yuan, Z., El‐Mesery, H. S., Shi, J., & Zou, X. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110998 · Cited by 8 · see citing works

Cognitive Architectures in Autonomous Robotics: A Systematic Review of Behavior Generation Approaches and Evaluation Strategies

González-Santamarta, M. Á., Rodríguez-Lera, F. J., & Olivera, V. M. (2025) · IEEE Access · doi:10.1109/access.2025.3630344 · Cited by 8 · see citing works

Toward the next frontier of embodied AI

Fan, R., Sun, M., & Giakos, G. (2025) · Intelligence & Robotics · doi:10.20517/ir.2025.44 · Cited by 8 · see citing works

Embodied intelligence: Recent advances and future perspectives

An, Z., Yu, X., Wang, C., Zhang, Y., & Song, C. (2025) · The Innovation Informatics · doi:10.59717/j.xinn-inform.2025.100008 · Cited by 7 · see citing works

MechRAG: a multimodal large language model for mechanical engineering

Li, S., & Corney, J. (2025) · Communications Engineering · doi:10.1038/s44172-025-00517-z · Cited by 8 · see citing works

Large language models in human-robot collaboration: A systematic review, trends, and challenges

Yuan, G., Liu, X., Xiao, M., Xiao, J., & Wang, L. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.01.011 · Cited by 9 · see citing works

Robotic prefab 3D printing buildings in extreme environments toward Martian habitats

Cai, G., Sun, L., Xu, H., Jiang, Q. M., Yang, Y., Lam, P. M., Lu, F., Yao, Y., Guo, Z., Ma, D., et al. (2026) · npj Space Exploration · doi:10.1038/s44453-025-00025-6 · Cited by 7 · see citing works

WaterRAG: A Multiagent Retrieval-Augmented Generation Framework to Support Water Industry Transitions to Net-Zero

Zhai, M., Zeng, A., Qiu, R., Li, J., Zhu, Q., Waite, T. D., Ni, B., & Duan, H. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.5c15806 · Cited by 7 · see citing works

The Touch-Code Glove: a multimodal mapping interface with triboelectric-digital encoding for intuitive robot training

Sun, Y., Li, D., Yang, R., Zhou, Z., Ji, T., Lu, B., Sun, L., & Liu, H. (2025) · Soft Science · doi:10.20517/ss.2025.68 · Cited by 6 · see citing works

Learning Precise Affordances From Egocentric Videos for Robotic Manipulation

Li, G., Tsagkas, N., Song, J., Mon-Williams, R., Vijayakumar, S., Shao, K., & Sevilla-Lara, L. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00985 · Cited by 6 · see citing works

Vision language model-enhanced embodied intelligence for digital twin-assisted human-robot collaborative assembly

Liu, C., Tang, D., Zhu, H., Zhang, Z., Wang, L., & Zhang, Y. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100943 · Cited by 5 · see citing works

A novel paradigm of robotic machining towards embodied intelligent manufacturing: Case study on paint defect repair

Wang, S., Tan, Z., Wang, Y., Zhou, Z., & Zhu, D. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103184 · Cited by 4 · see citing works

When control meets large language models: From words to dynamics

Nosrati, K., Tepljakov, A., Belikov, J., & Petlenkov, E. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115119 · Cited by 6 · see citing works

DK-SMF: Domain Knowledge-Driven Semantic Modeling Framework for Service Robots

Joo, K., Jeong, Y., Kwon, S., Jeong, M., Kim, H., & Kuc, T. (2025) · Electronics · doi:10.3390/electronics14163197 · Cited by 4 · see citing works

Adaptive Large Language Model for Task Orchestration in 6G Space-Air-Ground Integrated Computing Power Networks

Li, W., Tang, F., Zhao, M., Omachi, M., & Kato, N. (2025) · IEEE Transactions on Network Science and Engineering · doi:10.1109/tnse.2025.3644352 · Cited by 3 · see citing works

Large Language Models in Sensor-Driven Control Systems: Architectures, Challenges, and Opportunities

Aghaee, F., & Shaker, H. R. (2026) · Sensors · doi:10.3390/s26144350 · Cited by 3 · see citing works

An AI Agent for cell-type specific brain computer interfacesPreprint

Marin‐Llobet, A., Lin, Z., Baek, J., Aljović, A., Zhang, X., Lee, A. J., Wang, W., Lee, J. Y., Shen, H., He, Y., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.11.675660 · Cited by 3 · see citing works

DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agents

Du, M., Xu, B., Zhu, C., Wang, X., & Mao, Z. (2025) · arXiv · doi:10.48550/arxiv.2506.11763 · Cited by 2 · see citing works

Healthcare reimagined: Carebots from hospital to home

Kaliya-Perumal, A.-K., & Sidarta, A. (2025) · Public Health · doi:10.1016/j.puhe.2025.105920 · Cited by 2 · see citing works

Using Visual Language Models to Control Bionic Hands: Assessment of Object Perception and Grasp Inference

Karaali, O., Farag, H. M., Dosen, S. S., & Stefanović, Č. (2025) · International Conference on Information, Communication and Automation Technologies (ICAT) · doi:10.1109/icat66432.2025.11189268 · Cited by 2 · see citing works

Orchestrating Embodied Systems through the Embodied Context Protocol: Motivation, Progress, and Directions

Ma, F., Li, D., Chen, Y., Xing, J., Liu, Y., Lan, D., Shao, J., & Pang, Z. (2025) · Research · doi:10.34133/research.1047 · Cited by 3 · see citing works

Physics-Aware Multichannel Vector Quantization for Hybrid Digital Twin Modeling of UAV Systems

Tu, J., Nian, X., & Dai, X. (2025) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2025.3639005 · Cited by 2 · see citing works

AI-native robotic vision systems enabled by in-sensor computing

Kim, D., Kwon, J. I., Kim, Y., Kim, D., & Choi, C. (2026) · npj Unconventional Computing · doi:10.1038/s44335-025-00047-z · Cited by 2 · see citing works

Multimodal Large Language Models in Construction Education for Learning Human–Robot Collaboration: A Narrative Review

Olukanni, E. O., Akanmu, A. A., & Jebelli, H. (2026) · ASCE OPEN Multidisciplinary Journal of Civil Engineering · doi:10.1061/aomjah.aoeng-0102 · Cited by 2 · see citing works

Artificial Intelligence of Things as a Foundation for Agentic AI Systems: Architectures, Applications, and Challenges

Gadekallu, T. R., Zhao, Y., Wen, Z., Bhattacharya, P., Xia, Y., Cai, J., Zeydan, E., Fang, K., & Feng, H. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3703862 · Cited by 3 · see citing works

A spike–language dual framework bridges fast perception and deep reasoning in artificial tactile somatosensory systems

Sun, H., Gao, X., Niu, W., Li, L., Song, Y., Liu, L., Zhou, H., Liu, H., Wang, Z. L., Pu, X., et al. (2026) · Nature Sensors · doi:10.1038/s44460-026-00108-1 · Cited by 2 · see citing works

Large language models in intelligent manufacturing and mechanical engineering: a review of robotics, fault diagnosis, design, and engineering knowledge workflows

Sorour, S. S., & Sahbel, A. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-026-02927-y · Cited by 2 · see citing works

AI for science: Progress, challenges, and perspectives

Xu, Y., Shao, Z., Liu, X., Li, Y., An, Z., Fu, C., Huang, C., Zhang, L., Gao, F., Li, J., et al. (2026) · The Innovation · doi:10.1016/j.xinn.2026.101530 · Cited by 3 · see citing works

Hinst: Human‐Like Interactive Instinct Enables Robots to Robustly Accomplish Universal Tasks

Liao, Z., Mao, Q., Qin, Y., Yuan, J., & Zhu, R. (2025) · Advanced Science · doi:10.1002/advs.202509483 · Cited by 1 · see citing works

BeetleBrush: A bio-inspired and LLM-RAG-augmented framework for trajectory planning in robotic drawing tasks

Khan, A. T., Cao, X., Li, S., Yi, C., & Moshayedi, A. J. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132474 · Cited by 1 · see citing works

Extended Abstract: Will Embodied AI Become Sentient?

Lee, E. A. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-07132-3_1 · Cited by 1 · see citing works

Challenging issues in empowering smart agriculture develop- ment with control science

Hongyi, L. I., Xing, H. E., Xin, W. A. N. G., Huaqing, L. I., Shouyong, X. I. E., & JinJun, W. A. N. G. (2025) · Scientia Sinica Informationis · doi:10.1360/ssi-2025-0350 · Cited by 1 · see citing works

Cross-Modality Alignment Perception and Multi-Head Self-Attention Mechanism for Vision-Language-Action of Humanoid Robot

Ren, B. S., & Shi, D. (2025) · Sensors · doi:10.3390/s26010165 · Cited by 1 · see citing works

LanPerAct: A Framework for Language-Driven Perception and Robotic Manipulation

Wu, Z., Wu, S., Tu, Y., Zhou, H., Drew, S., & Niu, X. (2025) · IEEE Smart World Congress (SWC) · doi:10.1109/swc65939.2025.00095 · Cited by 1 · see citing works

A framework for VLM-knowledge graph integration in complex long-horizon tasks

Yin, L., Cheng, Y., Du, X., Tong, N., Xu, F., & Li, F. (2026) · Knowledge-Based Systems · doi:10.1016/j.knosys.2026.115588 · Cited by 1 · see citing works

Oracle-grasp: zero-shot affordance-aligned robotic grasping using large multimodal models

Giuili, A., Atari, R., & Sintov, A. (2026) · Intelligent Service Robotics · doi:10.1007/s11370-026-00707-4 · Cited by 1 · see citing works

A multimodal LLM-driven robotic control system for adaptive industrial manipulation: Integrating vision-language models for enhanced manufacturing flexibility

Dai, W., Zhou, X., Wu, P., Zhou, J., Shang, Y., & Fan, Y. (2026) · Cognitive Robotics · doi:10.1016/j.cogr.2026.03.002 · Cited by 1 · see citing works

Sense, think, and robot control: Multimodal material recognition sensing integrated with large language model reasoning

Wang, Z., Zhang, F., Lin, Q., Gao, Y., Sun, B., Ke, H., Liu, N., Tian, B., Zhao, L., & Jiang, Z. (2026) · Chemical Engineering Journal · doi:10.1016/j.cej.2026.176909 · Cited by 1 · see citing works

Proactive collaboration via autonomous interaction

Li, P., Zhang, X., Yang, W., Hu, Y., Wang, Y., Wang, D., Li, H., Guo, D., & Liu, H. (2026) · Nature Communications · doi:10.1038/s41467-026-72797-8 · Cited by 1 · see citing works

Autonomous Embodied AI

Wang, X., Feng, T., Liu, H., & Zhu, W. (2026) · Springer eBooks · doi:10.1007/978-981-95-7750-7_2 · Cited by 1 · see citing works

Vision language model-enhanced embodied intelligence for AR-assisted HRC assembly: Multimodal cognition, task reasoning, and autonomous execution

Liu, C., Tang, D., Zhu, H., Wang, L. N., Cai, Q., & Shi, H. (2026) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2026.06.021 · Cited by 1 · see citing works

PFEA: a VLM-based high-level natural language planning and feedback embodied agent for human-centered AI

Ding, W., Chen, J., Chen, M., Xie, F., Wang, J., Yang, S. X., & Dames, P. (2026) · Frontiers in Robotics and AI · doi:10.3389/frobt.2026.1905578 · Cited by 1 · see citing works

Socially Aware Robot Navigation With Meta Reinforcement Learning: A Field-Theory Perspective

Lu, S., Chen, B., Zhong, P., Zou, T., Yang, H., Liu, R., & Sheng, Y. (2025) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2025.3616393 · Cited by 1 · see citing works

A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents

Huang, W.-C., Zhang, W., Liang, Y., Bei, Y., Chen, Y., Feng, T., Pan, X., Tan, Z., Wang, Y., Wei, T., et al. (2026) · arXiv · doi:10.48550/arxiv.2602.06052

Data Driven Interface Regulated Wearable 3d Motion Communicator for Human Finger ElectronicsPreprint

Tai, Y., Wang, Q., Xiang, Z., Qi, B., Guo, H., Xiao, C., Zhou, X., Yang, Z., Deng, X., Li, G., et al. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5353979

Integración modular de herramientas gestionadas por LLMs en el robot TIAGo++

Menéndez, E., García-Haro, J. M., Martínez, S., & Balaguer, C. (2025) · Jornadas de Automática · doi:10.17979/ja-cea.2025.46.12229

A Dharmakīrtian Model of Relevance Realization in Cognitive Agents

Amir, N., & Dunne, J. D. (2025) · Contemporary Buddhism · doi:10.1080/14639947.2025.2564476

Design and Implementation of a Delivery Robot System for Natural Language Interaction

Feng, Q., Zhang, G., & Chen, K. (2025) · International Conference on Internet of Things, Automation and Artificial Intelligence (IoTAAI) · doi:10.1109/iotaai66837.2025.11213046

Extracting Commonsense Knowledge for Robotic Agents from LLMs

Adamik, M., Tiddi, I., & Stefan Schlobach, A. (2025) · International Conference on Knowledge Capture · doi:10.1145/3731443.3771358

Context-Aware Small Language Models for Real Time Strategy Prediction in Strategy Game

Admajindra, C. H., & Haryanto, H. (2025) · International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) · doi:10.1109/icimcis68501.2025.11327013

Research on Interactive Command Generation Methods for Virtual Masonry by Robots

Xu, J., Li, Y., Zhong, J., Jiang, Y., & Ma, J. (2025) · International Conference on Robotics and Automation Engineering (ICRAE) · doi:10.1109/icrae67496.2025.00020

An Effective 6DoF Grasp Detection Framework for Human–Robot Interaction With LLMs

Yu, S. W., Zhai, D., & Xia, Y. (2026) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2026.3656994

BACON: A Body-Aware Framework for Parameterized Collision Detection Acceleration

Xing, Y., Feng, D., Li, H., Ji, X., & Zhao, R. J. (2026) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2026.3664290

An approach to using large language models with augmented search to improve the interpretability of machine learning models

Ivanov, R. D., Менисов, А. Б., Mishukov, O. A., & Sabirov, T. R. (2026) · Scientific and technical journal of information technologies mechanics and optics · doi:10.17586/2226-1494-2026-26-1-69-76

Aligning Task Goals before Execution: Insights from Diverse User Groups into Human–Robot Communication in Domestic Settings

Jia, L., Ye, Y., Styler, B. K., & Du, N. (2026) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3757279.3788662

Look2React: Making VR NPCs Come Alive with Dynamic Vision-Guided Reactions

Vatsal, R., Huang, X., & Xiao, R. (2026) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2026.3679121

Zero-Shot Task Automation with Foundation Language Models

Divesh Kumar, S., I, D. A. A., Maharasi, M., & Pavithra, S. (2025) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec67334.2025.11459707

Future of Robotics and Integration of Artificial Intelligence

Giménez, M., Innocenzi, C., Espínola, F., Forgione, A., & Marescaux, J. (2026) · Surgical Oncology Clinics of North America · doi:10.1016/j.soc.2025.12.003

Mapping the Wizards' Path: A Systematic Review of Wizard-of-Oz in HCI

Yang, R., Du, Y., Du, H., & Huang, K. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791174

Task planning for dual dexterous hand collaboration via scene understanding and semantic information

Li, B., Li, B., Gao, T., Wang, H., & Zou, S. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae602b

A retrieval-augmented framework enabling VLM spatial awareness for object-centric robot manipulation

Chen, K., Li, C., Tu, C., Pan, J., Ma, Y., Chen, W., Zhou, Z., Xu, X., James, S., Fu, C., et al. (2026) · Science Robotics · doi:10.1126/scirobotics.aea2092

Agents Trainer: Automatically Training Multi-Agent Reinforcement Learning Models for Drone Swarm Using Language Model-Based Agents

Lou, J., Shi, R., Wang, H., Yu, M.-M., Wang, Y., Wang, Q., & Wu, W. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3689345

Behavior-Driven Marine Larval Dispersal and Settlement with AI Agent-Based ModelingPreprint

Zhou, X., Wang, G., Wu, R., & Bracco, A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.29.721765

On the Availability Risks of Production LLM Services Under Unbounded Inference

Li, Y., Wang, J., Zhu, H., Lin, J., Chang, S., & Guo, M. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3689535

Ethics of Social Robotics: Co-evolution or the Extended Mind?

Damiano, L., & Dumouchel, P. (2026) · Springer eBooks · doi:10.1007/978-3-032-15112-4_52

A cloud-edge collaborative framework with confidence-aware perception and cognitive dual-stream memory for proactive service robot decision-making

Jiang, S., Wang, Y., & Yuan, X. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.132623

Basic Research on Physical Intelligent System for Extreme Environment

Wang, S., Du, X., Duan, H., Yu, H., Da, L., Pan, F., Liu, H., Liu, H., Cao, H., & Wu, F. (2026) · 中国科学基金 · doi:10.3724/bnsfc-2025-0154

Two Revolutions, No Bridge: The Absence of a Connecting Framework Between Sociometabolic Energy Regimes and the AI TransitionPreprint

Milou, A. C. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6679558

Perceptual Interaction System of Elderly Care Robot Based on Multimodal Large Language Models and Distributed Computing

Wang, A., Wang, K., Wang, Y., Yue, X., Li, H., & Yao, Y. (2026) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2026.p0781

MaCoPlanner: LLM-Assisted Manual-Compiled Task Planning with Proactive Safety Verification for Robotic Industrial Panel OperationPreprint

Xin, G., Xu, J., Deghat, M., Wan, C., Liu, J., Hu, Y., & Hu, Z. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6799077

A Review of Active Collaborative Perception and Planning for Heterogeneous UAV-UGV Systems

Sun, W. (2026) · International Conference on Advanced Electronic Materials, Computers and Software Engineering (AEMCSE) · doi:10.1109/aetcse69203.2026.11504015

LaViCoN: Language and Vision Cooperative Navigation for Assistive Mobile Robots

Yang, T. Y., Zhong, R., & Shi, W. (2026) · IEEE International Conference on Mobility, Operations, Services and Technologies (MOST) · doi:10.1109/most69733.2026.00016

Sentinel: Embodied Cooperative Spatial Reasoning and Planning

Lin, X., Zhang, H., Deng, R., Zhou, Q., & Gan, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-37020-4_36

Gate^2-VLA: Regulating Attention Routing for Long-Horizon VisionLanguage-Action ModelPreprint

ZHANG, J., ZHANG, Y., Sun, H., & Hashimoto, K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6842461

Real-Time Edge Semantics for Drone Swarms via FPGA Perception and On-Device LLMs

Wang, Z., Fortney, W. A., Forcha, P., Feng, Y., Bendix, G., & Bobda, C. (2026) · International Symposium on Quality Electronic Design (ISQED) · doi:10.1109/isqed69900.2026.11534722

Editorial for the Special Issue “Robotic Intelligence Development of AI in Robot Perception, Learning, and Decision”

Peng, Y. K. M., & Gómez-Bravo, F. (2026) · Machines · doi:10.3390/machines14060650

A Robotic Disassembly Planning Method for Retired Batteries Based on a Long Short-Term Memory Collaborative Framework

Li, J., Zhang, X., Si, J., & Bao, J. (2026) · Symmetry · doi:10.3390/sym18060981

How can reasoning capability empower the AI copilot robot in endoscopic surgery

Wang, G., Bai, L., & Ren, H. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02827-8

Integrating Unconventional Approaches for Intelligent and Adaptive Soft Robotic Systems

Jeong, H., & Paik, J. K. (2026) · Springer eBooks · doi:10.1007/978-3-032-22453-8_12

Embodied AI for kitchen scene rearrangement: a deep spatio-temporal approach

Esfahani, A. N., Hosseini, H. M., Masouleh, M. T., & Kalhor, A. (2026) · Intelligent Service Robotics · doi:10.1007/s11370-026-00723-4

LLMs as Research Instruments for Problems in Engineering Management: A Context Modeling Framework

Qin, R., Mansouri, A., Zhen, D., Erfani, A., & Cui, Q. (2026) · Journal of Management in Engineering · doi:10.1061/jmenea.meeng-7519

Robust MLLM-Based Decision Making for Swarm Confrontation Under Uncertain Constraints

WANG, L. C., & Chen, L. (2026) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc69976.2026.11560248

Brain-Inspired Generative AI Revives Legacy Industrial Robots Toward Sustainable Automation

Liu, Z., Wang, T., Zhong, R., Wang, X. V., Li, M., & Wang, L. (2026) · Engineering · doi:10.1016/j.eng.2026.05.020

Indoor Lighting Intelligent Control Using Context-Aware-Based LLM Agent and Domain Knowledge Base

Wang, Y. C., Sun, F., & Fang, Q. (2026) · Light & Engineering · doi:10.33383/2025-068

Verification-gated agentic mission-state governance for intelligent industrial multi-robot systems

Tang, G., Jia, Q., Tan, Y., Huang, Z., Ji, N., & Chen, G. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.116216

Doc2DT: An LLM-Driven Knowledge Graph Abstraction Framework for Digital Twin Generation from Unstructured Engineering DocumentsPreprint

Lee, Y., Choi, S., & Lee, S. W. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7079738

Agentic mobile AI systems: From post-hoc optimization to runtime-elastic realization

Liu, S., Wu, W., Xu, X., Li, T., Pang, B., Zhou, W., Guo, B., & Yu, Z. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104630

Auditable LLM-guided Pareto scenario discovery and hard-bank fine-tuning for robust map-free LiDAR navigation

Yılmaz, T., & Aydoğmuş, Ö. (2026) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2026.105628

On AI-Based Critical Systems’ Embedded LLM Trustworthiness: A Real-Time Analytical Filters for LLM Output Control

Fourastier, Y., & Chaouchi, H. (2026) · Springer proceedings in complexity · doi:10.1007/978-3-032-21524-6_41

Embodied Assistant: Robot Mobility Operations Guided by Open Vocabulary in Open Environments Utilizing LLM

Guo, Y., Ni, Y., Jin, J., Jiang, Y., Li, D., Zhao, H., & Shen, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3590433 · Cited by 5 · see citing works

A robot operating system framework for using large language models in embodied AI

Mower, C. E., Wan, Y., Yu, H., Grosnit, A., Gonzalez-Billandon, J., Zimmer, M., Liu, P., Palenicek, D., Tateo, D., Peters, J., et al. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01186-z · Cited by 24 · see citing works

Think Before Execute: Embodied Reasoning of Supportive Tools for Robot Service with Large Language Models

Liu, J., Fu, T., Zhou, F., & Wang, C. (2024) · IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio64047.2024.10907522

Enabling robots to follow abstract instructions and complete complex dynamic tasksPreprint

Mon-Williams, R., Li, G., Long, R., Du, W., & Lucas, C. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2406.11231 · Cited by 1 · see citing works

Large Language Models in Robotics: Applications, Challenges, and Future Directions from the Perspective of Embodied Intelligence

Wang, R. (2025) · Advances in Engineering Technology Research · doi:10.56028/aetr.15.1.1604.2025