The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Code as Policies: Language Model Programs for Embodied Control
Open accessAbstract
Large language models (LLMs) trained on code-completion have been shown to be capable of synthesizing simple Python programs from docstrings [1]. We find that these code-writing LLMs can be re-purposed to write robot policy code, given natural language commands. Specifically, policy code can express functions or feedback loops that process perception outputs (e.g., from object detectors [2], [3]) and parameterize control primitive APIs. When provided as input several example language commands (formatted as comments) followed by corresponding policy code (via few-shot prompting), LLMs can take in new commands and autonomously re-compose API calls to generate new policy code respectively. By chaining classic logic structures and referencing third-party libraries (e.g., NumPy, Shapely) to perform arithmetic, LLMs used in this way can write robot policies that (i) exhibit spatial-geometric reasoning, (ii) generalize to new instructions, and (iii) prescribe precise values (e.g., velocities) to ambiguous descriptions (‘faster’) depending on context (i.e., behavioral commonsense). This paper presents Code as Policies: a robot-centric formulation of language model generated programs (LMPs) that can represent reactive policies (e.g., impedance controllers), as well as waypoint-based policies (vision-based pick and place, trajectory-based control), demonstrated across multiple real robot platforms. Central to our approach is prompting hierarchical code-gen (recursively defining undefined functions), which can write more complex code and also improves state-of-the-art to solve 39.8% of problems on the HumanEval [1] benchmark. Code and videos are available at https://code-as-policies.github.io
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“LLM-generated robotic task plans are highly sensitive to prompt phrasing (Liang et al., 2023) and vulnerable to adversarial manipulation (Zhang et al., 2024c).”
From Large Language Model Reasoning Failures (2026) · cites this paper as Liang et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Naive LLMs and VLMs generate plans step by step, lacking mechanisms to detect and correct earlier mistakes or execution errors (Liang et al., 2023; Huang et al., 2022b; Duan et al., 2024).”
From Large Language Model Reasoning Failures (2026) · cites this paper as Liang et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Incorporating feedback mechanisms or explicit error-handling strategies significantly reduces these errors (Liang et al., 2023; Wang et al., 2023a).”
From Large Language Model Reasoning Failures (2026) · cites this paper as Liang et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In personalization and embodied control, agents author and revise executable policies, simulators, and skill libraries in response to environment feedback [9, 10, 32].”
From Code as Agent Harness (2026) · cites this paper as [10]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Programs are increasingly used as the medium through which LLM agents reason, act, and model their environments.”
From Code as Agent Harness (2026) · cites this paper as [10]
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Code and classical robotics software define the admissibility boundary by exposing typed robot APIs, parameterizing primitive skills, calling geometric libraries, invoking motion planners, and supporting inspection, replay, versioning, and verification [124, 10, 434, 435].”
From Code as Agent Harness (2026) · cites this paper as [10]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Whereas previous work needed to compose multiple models to this end (Ahn et al., 2022; Kwon et al., 2024; Liang et al., 2023; Vemprala et al., 2023)”
From Gemini Robotics: Bringing AI into the Physical World (2025) · cites this paper as Liang et al., 2023
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Trained on large datasets of images and language, VLMs can provide powerful visual semantic understanding and useful guidance to robotic tasks, such as which object a manipulator should pick up or which goal a mobile robot should reach [8, 9, 10].”
From RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics (2024) · cites this paper as [9]
The cited source says what the claim attributes to it.
In the citing paper
“Several works [8, 9, 10] have shown that language model are capable planners for robotic tasks.”
From RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics (2024) · cites this paper as [9]
The cited source says what the claim attributes to it.
In the citing paper
“We opt to use code as the action space instead of low-level motor commands because programs can naturally represent temporally extended and compositional actions [16, 22], which are essential for many long-horizon tasks in Minecraft.”
From Voyager: An Open-Ended Embodied Agent with Large Language Models (2023) · cites this paper as [16]
The cited source says what the claim attributes to it.
In the citing paper
“robotic and embodied agents use generated programs as executable policies for interacting with physical or simulated worlds [9, 10]”
From Code as Agent Harness (2026) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“Recent advances in large language model (LLM) based agents harness the world knowledge encapsulated in pre-trained LLMs to generate consistent action plans or executable policies [16, 22, 19].”
From Voyager: An Open-Ended Embodied Agent with Large Language Models (2023) · cites this paper as [16]
The cited source says what the claim attributes to it.
In the citing paper
“As a grounding interface, it translates high-level intent from LLMs into embodiment-respecting commands through primitive skill calls [9, 110, 111, 112], synthesized Python control policies [10, 33, 124, 117, 118], and structured behavior-tree programs [34].”
From Code as Agent Harness (2026) · cites this paper as [10]
The cited source says what the claim attributes to it.
In the citing paper
“Many existing approaches leveraging foundation models include CodeAsPolicies [6], Voyager [35], VIMA [92], VoxPoser [77].”
From Embodied Agent Interface: Benchmarking LLMs for Embodied Decision Making (2024) · cites this paper as [6]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“there has been a performance revolution of sorts there (Yao et al., 2023b;Liang et al., 2023;Wang et al., 2023b).”
From LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks (2024) · cites this paper as Liang et al., 2023
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q. V., & Zhou, D. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1800 · Cited by 4,505
Self-citationTraining language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
PaLM: Scaling Language Modeling with Pathways
Chowdhery, A., Narang, S., Devlin, J., Bosma, M., Mishra, G., Roberts, A., Barham, P., Chung, H. W., Sutton, C., Gehrmann, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2204.02311 · Cited by 2,590
Large language models are zero-shot reasoners
Kojima, T., Gu, S. (., Reid, M., Matsuo, Y., & Iwasawa, Y. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-1613 · Cited by 2,013
Open-vocabulary Object Detection via Vision and Language Knowledge DistillationPreprint
Gu, X.-Y., Lin, T.-Y., Kuo, W.-C., & Cui, Y. (2021) · International Conference on Learning Representations · doi:10.48550/arxiv.2104.13921 · Cited by 1,464
Evaluating Large Language Models Trained on Code
Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H. P. D. O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al. (2021) · arXiv · doi:10.48550/arxiv.2107.03374 · Cited by 1,436
Understanding Natural Language Commands for Robotic Navigation and Mobile Manipulation
Tellex, S., Kollar, T., Dickerson, S., Walter, M. R., Banerjee, A., Teller, S. J., & Roy, N. (2011) · AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v25i1.7979 · Cited by 903
MDETR - Modulated Detection for End-to-End Multi-Modal Understanding
Kamath, A., Singh, M., LeCun, Y., Synnaeve, G., Misra, I., & Carion, N. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00180 · Cited by 802
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
Self-citation
Research works printed but not linked: 2
- cideron, 2019, Self-educated language agent with hindsight experience replay for instruction following, DeepMind
- kalashnikov, 2018, Scalable deep reinforcement learning for vision-based robotic manipulation, CoRL
Cited by
Sorted by citations
Items 201–300 of 853
Schelble, B. G., Flathmann, C., Macdonald, J. P., Knijnenburg, B. P., Brady, C., & McNeese, N. J. (2024) · Behaviour and Information Technology · doi:10.1080/0144929x.2024.2396476 · Cited by 18
Embodied AI with Two Arms: Zero-shot Learning, Safety and Modularity
Varley, J., Singh, S., Jain, D., Choromański, K., Zeng, A., Chowdhury, S. B. R., Dubey, A., & Sindhwani, V. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802181 · Cited by 29
Self-citationV-IRL: Grounding Virtual Intelligence in Real Life
Yang, J., Ding, R., Brown, E., Qi, X., & Xie, S. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-72995-9_3 · Cited by 20
Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-Guided 3D Policy
Garcia, R., Chen, S., & Schmid, C. (2025) · 2025 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra55743.2025.11127315 · Cited by 14
Kim, K., Ghimire, P., & Huang, P.-C. (2025) · Robotics · doi:10.3390/robotics14090117 · Cited by 15
Leveraging LLM Agents and Digital Twins for Fault Handling in Process Plants
Gill, M. S., Vyas, J., Markaj, A., Gehlhoff, F., & Mercangöz, M. (2025) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa65518.2025.11205597 · Cited by 15
Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback
Bhat, V., Kaypak, A. U., Krishnamurthy, P., Karri, R., & Khorrami, F. (2025) · Advanced Robotics Research · doi:10.1002/adrr.202500072 · Cited by 38
DENG, H., Namoano, B., ZHENG, B., Khan, S., & Erkoyuncu, J. A. (2024) · PHM Society European Conference · doi:10.36001/phme.2024.v8i1.4114 · Cited by 17
Geng, H., Wei, S.-L., Deng, C.-Y., Shen, B., Wang, H., & Guibas, L. J. (2024) · Robotics: Science and Systems Conference · doi:10.15607/rss.2024.xx.016 · Cited by 16
Large Language Models for Orchestrating Bimanual Robots
Chu, K., Zhao, X., Weber, C., Li, M., Lu, W., & Wermter, S. (2024) · IEEE-RAS International Conference on Humanoid Robots (Humanoids) · doi:10.1109/humanoids58906.2024.10769891 · Cited by 17
Instance-Aware Visual Language Grounding for Consumer Robot Navigation
Huang, J., Zhang, H., Zhao, M., Wu, Z., & Liu, Y. (2025) · IEEE Transactions on Consumer Electronics · doi:10.1109/tce.2025.3601582 · Cited by 14
KALM: Keypoint Abstraction Using Large Models for Object-Relative Imitation Learning
Fang, X., Huang, B.-R., Mao, J., Shone, J., Tenenbaum, J. B., Lozano‐Pérez, T., & Kaelbling, L. P. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128681 · Cited by 14
What foundation models can bring for robot learning in manipulation: A survey
Li, D., Jin, Y., Sun, Y.-H., Yong, A., Yu, H., Shi, J., Hao, X., Peng, H., Liu, H., Li, X., et al. (2025) · The International Journal of Robotics Research · doi:10.1177/02783649251390579 · Cited by 36
VLATest: Testing and Evaluating Vision-Language-Action Models for Robotic Manipulation
Wang, Z., Zhou, Z., Song, J., Huang, Y., Shu, Z., & Ma, L. (2025) · Proceedings of the ACM on Software Engineering · doi:10.1145/3729343 · Cited by 14
Hori, K., Suzuki, K., & Ogata, T. (2024) · IEEE/SICE International Symposium on System Integration (SII) · doi:10.1109/sii58957.2024.10417267 · Cited by 15
Toward Automated Programming for Robotic Assembly Using ChatGPT
Macaluso, A., Cote, N., & Chitta, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610554 · Cited by 19
BlendSQL: A Scalable Dialect for Unifying Hybrid Question Answering in Relational Algebra
Glenn, P., Dakle, P. P., Wang, L., & Raghavan, P. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.25 · Cited by 23
TalkWithMachines: Enhancing Human-Robot Interaction Through Large/Vision Language Models
Abbas, A. N., & Beleznai, C. (2024) · IEEE International Conference on Robotic Computing (IRC) · doi:10.1109/irc63610.2024.00039 · Cited by 15
Liu, Y., Liu, L., Zheng, Y., Liu, Y., Dang, F., Li, N., & Ma, K. (2025) · Science China Information Sciences · doi:10.1007/s11432-024-4303-8 · Cited by 16
Reinforced Intelligence Through Active Interaction in Real World: A Survey on Embodied AI
Kwon, W., Baek, S., Baek, J., Shin, W., Gwak, M., Park, P., & Lee, S. (2025) · International Journal of Control Automation and Systems · doi:10.1007/s12555-025-0127-1 · Cited by 13
AutoVFX: Physically Realistic Video Editing from Natural Language Instructions
Hsu, H.-Y., Lin, C., Zhai, A. J., Xia, H., & Wang, S. (2025) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv66043.2025.00076 · Cited by 33
ExploRLLM: Guiding Exploration in Reinforcement Learning with Large Language Models
Ma, R.-Y., Luijkx, J., Ajanović, Z., & Kober, J. (2024) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra55743.2025.11127622 · Cited by 27
(2024) · doi:10.1109/icra57147.2024.10611448 · Cited by 12
Nadizar, G., Medvet, E., Nichele, S., & Pontes-Filho, S. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110610 · Cited by 13
Enhancing smart home interaction through multimodal command disambiguation
Calò, T., & Russis, L. D. (2024) · Personal and Ubiquitous Computing · doi:10.1007/s00779-024-01827-3 · Cited by 17
SARA-RT: Scaling up Robotics Transformers with Self-Adaptive Robust Attention
Leal, I. M. P., Choromański, K., Jain, D., Dubey, A., Varley, J., Ryoo, M. S., Lu, Y., Liu, F., Sindhwani, V., Vuong, Q., et al. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611597 · Cited by 16
Self-citationSuFIA: Language-Guided Augmented Dexterity for Robotic Surgical Assistants
Moghani, M., Doorenbos, L., Panitch, W. C.-H., Huver, S. D., Azizian, M., Goldberg, K., & Garg, A. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802053 · Cited by 12
Language-Guided Object-Centric Diffusion Policy for Generalizable and Collision-Aware Manipulation
Li, H., Feng, Q., Zheng, Z., Feng, J., Chen, Z., & Knoll, A. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127231 · Cited by 20
Programming-by-Demonstration for Long-Horizon Robot Tasks
Patton, N., Rahmani, K., Missula, M., Biswas, J., & Dillig, I. (2024) · Proceedings of the ACM on Programming Languages · doi:10.1145/3632860 · Cited by 12
Language-Conditioned Robotic Manipulation with Fast and Slow Thinking
Zhu, M., Zhu, Y., Li, J., Wen, J., Xu, Z., Che, Z., Shen, C., Peng, Y., Liu, D., Feng, F., et al. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611525 · Cited by 21
LLMs Can't Plan, But Can Help Planning in LLM-Modulo Frameworks
Kambhampati, S., Valmeekam, K., Guan, L., Verma, M., Stechly, K., Bhambri, S., Saldyt, L., & Murthy, A. (2024) · arXiv · doi:10.48550/arxiv.2402.01817 · Cited by 237
Ross3d: Reconstructive Visual Instruction Tuning With 3D-Awareness
Wang, H., Zhao, Y., Wang, T., Fan, H., Zhang, X., & Zhang, Z. (2025) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51701.2025.00866 · Cited by 47
A Survey of Robotic Language Grounding: Tradeoffs between Symbols and Embeddings
Cohen, V., Liu, J. X., Mooney, R., Tellex, S., & Watkins, D. (2024) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2024/885 · Cited by 27
LVDiffusor: Distilling Functional Rearrangement Priors From Large Models Into Diffusor
Zeng, Y., Wu, M., Long, Y., Zhang, J., Ding, H., Cheng, H., & Dong, H. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3438036 · Cited by 12
Yow, J.-A., Garg, N. P., Ramanathan, M., & Ang, W. T. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1345693 · Cited by 18
Autonomous Behavior Planning For Humanoid Loco-manipulation Through Grounded Language Model
Wang, J., Laurenzi, A., & Tsagarakis, N. G. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802344 · Cited by 11
Xu, Z., Gao, C., Liu, Z., Yang, G., Tie, C., Zheng, H., Zhou, H., Peng, W., Wang, D., Hu, T., et al. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801782 · Cited by 11
General-Purpose Clothes Manipulation with Semantic Keypoints
Deng, Y., & Hsu, D. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128573 · Cited by 15
Jia, Z., Li, J.-J., Qu, X., & Wang, J.-Z. (2025) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra55743.2025.11127486 · Cited by 18
GPT-4.1 Sets the Standard in Automated Experiment Design Using Novel Python Libraries
Fachada, N., Fernandes, D. F. S., Fernandes, C. M., Ferreira-Saraiva, B. D., & Matos-Carvalho, J. P. (2025) · Future Internet · doi:10.3390/fi17090412 · Cited by 11
Long-horizon Locomotion and Manipulation on a Quadrupedal Robot with Large Language Models
Ouyang, Y., Li, J., Li, Y., Li, Z., Yu, C., Sreenath, K., & Wu, Y. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246632 · Cited by 32
Naderi, H., Shojaei, A., & Huang, L. (2026) · Eng—Advances in Engineering · doi:10.3390/eng7070358 · Cited by 18
Shirasaka, M., Matsushima, T., Tsunashima, S., Ikeda, Y., Horo, A., Ikoma, S., Tsuji, C., Wada, H., Omija, T., Komukai, D., et al. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611640 · Cited by 15
Wang, Z. Z., Mao, J., Fried, D., & Neubig, G. (2024) · arXiv · doi:10.48550/arxiv.2409.07429 · Cited by 5
“…from experiences with rule-based (Ellis et al., 2023; Bowers et al., 2023; Grand et al., 2023) or LM-based methods (Cai et al., 2023; Wang et al., 2024c;a) methods, and use them as auxiliary skills to ease future task-solving (Oh et al., 2017; Liang et al., 2023; Yu et al., 2023; Mao et al., 2023).”
MethodAutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models
Ouyang, S., & Li, L. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.205 · Cited by 12
Kim, K., Windle, J. R., Christian, M., Windle, T., Ryherd, E. E., Huang, P.-C., Robinson, A., & Chapman, R. (2024) · Applied Sciences · doi:10.3390/app14219922 · Cited by 10
Open Digital Rights Enforcement framework (ODRE): From descriptive to enforceable policies
Cimmino, A., Cano-Benito, J., & García‐Castro, R. (2024) · Computers & Security · doi:10.1016/j.cose.2024.104282 · Cited by 10
Zheng, W., & Arakawa, Y. (2025) · IEEE Access · doi:10.1109/access.2025.3598088 · Cited by 11
From connectivity to autonomy: the dawn of self-evolving communication systems
Nezami, Z., Shah, S. D. A., Hafeez, M., Djemame, K., & Zaidi, S. A. R. (2025) · Frontiers in Communications and Networks · doi:10.3389/frcmn.2025.1606493 · Cited by 10
Ryu, K., Liao, Q., Li, Z., Delgosha, P., Sreenath, K., & Mehr, N. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128783 · Cited by 29
RoboGrind: Intuitive and Interactive Surface Treatment with Industrial Robots
Alt, B., Stöckl, F., Müller, S., Braun, C., Raible, J., Alhasan, S., Rettig, O., Ringle, L., Katić, D., Jäkel, R., et al. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611143 · Cited by 10
LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement
Chang, H., Gao, K., Boyalakuntla, K., Lee, A. J., Huang, B., Yu, J., & Boularias, A. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802562 · Cited by 16
GenCHiP: Generating Robot Policy Code for High-Precision and Contact-Rich Manipulation Tasks
Burns, K., Jain, A., Go, K., Xia, F., Stark, M., Schaal, S., & Hausman, K. (2024) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros58592.2024.10801525 · Cited by 23
Self-citationIntegrating ChatGPT with Blockly for End-User Development of Robot Tasks
Gargioni, L., & Fogli, D. (2024) · IEEE/ACM International Conference on Human-Robot Interaction · doi:10.1145/3610978.3640653 · Cited by 10
Chen, X., Cai, Y., Mao, Y., Li, M., Yang, W., Xu, W., & Wang, J. (2024) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2024/755 · Cited by 9
Click to Grasp: Zero-Shot Precise Manipulation via Visual Diffusion Descriptors
Tsagkas, N., Rome, J., Ramamoorthy, S., Aodha, O. M., & Lu, C. X. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801488 · Cited by 9
LAMARS: Large Language Model-Based Anticipation Mechanism Acceleration in Real-Time Robotic Systems
Gao, Y., Luo, W. P., Wang, X., Zhang, S., & Goh, P. (2024) · IEEE Access · doi:10.1109/access.2024.3524906 · Cited by 10
Air-Ground Collaboration for Language-Specified Missions in Unknown Environments
Cladera, F., Ravichandran, Z., Hughes, J., Murali, V., Nieto-Granda, C., Hsieh, M. A., Pappas, G. J., Taylor, C. J., & Kumar, V. (2025) · IEEE transactions on field robotics. · doi:10.1109/tfr.2025.3584019 · Cited by 18
Autonomous Vehicle Maneuvering Using Vision–LLM Models for Marine Surface Vehicles
Kim, T., & Choi, W.-S. (2025) · Journal of Marine Science and Engineering · doi:10.3390/jmse13081553 · Cited by 9
Aghaee, F., & Shaker, H. R. (2025) · Aerospace Science and Technology · doi:10.1016/j.ast.2025.111259 · Cited by 9
Mohanaprasad, K., Rohith, G., Mondal, A., Singh, S., Tiwari, S., & Sharma, J. (2026) · IEEE Access · doi:10.1109/access.2026.3653992 · Cited by 9
From Pixels to Predicates: Learning Symbolic World Models via Pretrained VLMs
Athalye, A., Kumar, N., Silver, T., Liang, Y., Wang, J., Lozano‐Pérez, T., & Kaelbling, L. P. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3662533 · Cited by 9
Demonstrating Large Language Models on Robots
DeepMind, G. (2023) · Robotics: Science and Systems · doi:10.15607/rss.2023.xix.024 · Cited by 12
TRAD: Enhancing LLM Agents with Step-Wise Thought Retrieval and Aligned Decision
Zhou, R., Yang, Y., Wen, M., Wen, Y., Wang, W., Xi, C., Xu, G., Yu, Y. H., & Zhang, W. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3657788 · Cited by 18
Object-Centric Instruction Augmentation for Robotic Manipulation
Wen, J., Zhu, Y., Zhu, M., Li, J., Xu, Z., Che, Z., Shen, C., Peng, Y., Liu, D., Feng, F., et al. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10609992 · Cited by 13
HiCRISP: An LLM-Based Hierarchical Closed-Loop Robotic Intelligent Self-Correction Planner
Ming, C., Lin, J.-C., Fong, P.-K., Wang, H., Duan, X.-M., & He, J.-P. (2023) · ACM Cloud and Autonomic Computing Conference · doi:10.1109/cac63892.2024.10865457 · Cited by 7
Gemini Robotics: Bringing AI into the Physical World
Gemini Robotics Team, Abeyruwan, S., Ainslie, J., Alayrac, J.-B., Arenas, M. G., Armstrong, T., Balakrishna, A., Baruch, R., Bauza, M., Blokzijl, M., et al. (2025) · arXiv · doi:10.48550/arxiv.2503.20020 · Cited by 8
Self-citationEvaluating Spatial Understanding of Large Language Models
Yamada, Y., Bao, Y., Lampinen, A. K., Kasai, J., & Yildirim, I. (2023) · arXiv · doi:10.48550/arxiv.2310.14540 · Cited by 8
API-Assisted Code Generation for Question Answering on Varied Table Structures
Cao, Y., Chen, S., Liu, R., Wang, Z., & Fried, D. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.897 · Cited by 13
AutoPAC: Exploring LLMs for Automating Policy to Code Conversion in Business Organizations
Chowdhary, N., Dutta, T., Chattopadhyay, S., & Chakraborty, S. (2025) · International Conference on Communication Systems & Networks (COMSNETS) · doi:10.1109/comsnets63942.2025.10885751 · Cited by 10
Kim, T.-W., Kim, B., & Choi, J. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i4.32455 · Cited by 10
Object-Centric Prompt-Driven Vision-Language-Action Model for Robotic Manipulation
Li, X., Xu, J., Zhang, M., Liu, J., Shen, Y. F., Ponomarenko, I., Xu, J., Heng, L., Huang, S., Zhang, S., et al. (2025) · 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/cvpr52734.2025.02574 · Cited by 9
Xu, S., Luo, X., Huang, Y., Leng, L., Liu, R., & Liu, C. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3598648 · Cited by 10
Xie, G., Xu, J., Ding, Y., Zhang, Z., Zhang, S., & Li, Y. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11247231 · Cited by 8
Large Model in Low-Altitude Economy: Applications and Challenges
Hu, J., Wang, W., Liu, Y., & Zhang, J. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10010033 · Cited by 8
Coronado, E. (2026) · Robotics · doi:10.3390/robotics15030058 · Cited by 8
OPEx: A Component-Wise Analysis of LLM-Centric Agents in Embodied Instruction Following
Shi, H., Sun, Z., Yuan, X., Côté, M.-A., & Liu, B. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.37 · Cited by 17
Multi-modal LLM-enabled Long-horizon Skill Learning for Robotic Manipulation
Tan, R., Lou, S., Zhou, Y., & Lv, C. (2024) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/cis-ram61939.2024.10672715 · Cited by 8
Manas, K., Zwicklbauer, S., & Paschke, A. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801817 · Cited by 9
Zhao, Z., Yue, X., Xie, J., Fang, C., Shao, Z., & Guo, S. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3533476 · Cited by 9
Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models
Chen, A. S., Lessing, A. M., Tang, A., Chada, G., Smith, L. M., Levine, S., & Finn, C. (2024) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra55743.2025.11127234 · Cited by 25
On the Vulnerability of LLM/VLM-Controlled Robotics
Wu, X., Chakraborty, S., Xian, R., Liang, J., Guan, T., Liu, F., Sadler, B. M., Manocha, D., & Bedi, A. S. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246863 · Cited by 8
Self-citationEnhancing interpretability and interactivity in robot manipulation: A neurosymbolic approach
Tziafas, G., & Kasaei, H. (2026) · The International Journal of Robotics Research · doi:10.1177/02783649251412867 · Cited by 8
Language to Map: Topological map generation from natural language path instructions
Deguchi, H., Shibata, K., & Taguchi, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611377 · Cited by 8
Reactive Temporal Logic-based Planning and Control for Interactive Robotic Tasks
Nawaz, F., Peng, S., Lindemann, L., Figueroa, N., & Matni, N. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802335 · Cited by 7
LLM-drone: aerial additive manufacturing with drones planned using large language models
Merrill, C., Raman, A., George, A., & Farimani, A. B. (2025) · Construction Robotics · doi:10.1007/s41693-025-00162-0 · Cited by 10
Son, Y., Kim, M., Kim, S., Han, S., Kim, J., Jang, D., Yu, Y., & Park, C. Y. (2025) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2025.emnlp-main.1305 · Cited by 7
PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models
Guo, D., Xiang, Y., Zhao, S., Zhu, X., Tomizuka, M., Ding, M., & Zhan, W. (2025) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros60139.2025.11246481 · Cited by 14
YOLO-MARL: You Only LLM Once for Multi-Agent Reinforcement Learning
Zhuang, Y., Shen, Y., Zhang, Z., Chen, Y.-X., & Miao, F. (2024) · IEEE/RJS International Conference on Intelligent RObots and Systems · doi:10.1109/iros60139.2025.11245945 · Cited by 12
GenSwarm: Scalable Multi-Robot Code-Policy Generation and Deployment via Language Models
Ji, W., Chen, H., Chen, M., Zhu, G., Xu, L., Gros, R., Zhou, R., Cao, M., & Zhao, S. (2026) · npj Robotics · doi:10.1038/s44182-025-00065-w · Cited by 12
LINGO-Space: Language-Conditioned Incremental Grounding for Space
Kim, D., Oh, N., Hwang, D., & Park, D. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i9.28898 · Cited by 12
Empowering Large Language Models: Tool Learning for Real-World Interaction
Wang, H., Qin, Y., Lin, Y., Pan, J. Z., & Wong, K. (2024) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3626772.3661381 · Cited by 13
EAI-SIM: An Open-Source Embodied AI Simulation Framework with Large Language Models
Liu, G., Sun, T., Li, W., Li, X., Liu, X., & Cui, J. (2024) · International Conference on Control and Automation · doi:10.1109/icca62789.2024.10591865 · Cited by 7
INTRA: Interaction Relationship-Aware Weakly Supervised Affordance Grounding
Jang, J. H., Seo, H., & Chun, S. Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73039-9_2 · Cited by 7
Combining Ontological Knowledge and Large Language Model for User-Friendly Service Robots
Nakajima, H., & Miura, J. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802273 · Cited by 7
NARRATE: Versatile Language Architecture for Optimal Control in Robotics
Ismail, S., Arbues, A., Cotterell, R., Zurbrügg, R., & Alonso, C. A. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10801425 · Cited by 10
Hori, K., Suzuki, K., & Ogata, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-91448-4 · Cited by 8
SYNERGAI: Perception Alignment for Human-Robot Collaboration
Chen, Y., Zhang, I. G., Zhang, Y., Xu, H., Zhi, P., Li, Q., & Huang, S. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128658 · Cited by 10
A multi-agent-driven robotic AI chemist enabling autonomous chemical research on demandPreprint
Song, T., Luo, M., Chen, L., Huang, Y. H., Zhu, Q. J., Liu, D., Zhang, B., Zou, G., Zhang, F., Shang, W., et al. (2024) · ChemRxiv · doi:10.26434/chemrxiv-2024-w953h-v2 · Cited by 9
Goubard, C., & Demiris, Y. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611445 · Cited by 6
Metadata
- Authors
- Liang, J., Huang, W., Xia, F., Xu, P., Hausman, K., Ichter, B., Florence, P., & Zeng, A.
- Venue
- 2023 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2023-05-29
- Pages
- 9493-9500
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W4383097638
- Cited by
- 767
- References
- 59
- Citations by year
- 2026: 231 · 2025: 286 · 2024: 201 · 2023: 48
- FWCI
- 327.59
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Information Systems › Software Engineering Research
- Keywords
- large language models, code generation, robot policy, embodied control, natural language commands, few-shot prompting, trajectory-based control, impedance control
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
