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Code as Policies: Language Model Programs for Embodied Control
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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
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“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
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“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
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“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
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“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]
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“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]
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“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]
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“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
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“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]
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“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]
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“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]
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“robotic and embodied agents use generated programs as executable policies for interacting with physical or simulated worlds [9, 10]”
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“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].”
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“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].”
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“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]
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“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
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Opportunities of applying Large Language Models in building energy sector
Zhang, L., & Chen, Z. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.115558 · Cited by 60
Plug in the Safety Chip: Enforcing Constraints for LLM-driven Robot Agents
Yang, Z., Raman, S. S., Shah, A., & Tellex, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611447 · Cited by 84
LaMPilot: An Open Benchmark Dataset for Autonomous Driving with Language Model Programs
Ma, Y., Cui, C., Cao, X., Ye, W., Liu, P., Lu, J., Abdelraouf, A., Gupta, R., Han, K., Bera, A., et al. (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/cvpr52733.2024.01434 · Cited by 65
Gu, Z., Li, J., Shen, W., Yu, W., Xie, Z., McCrory, S., Cheng, X., Shamsah, A., Griffin, R. J., Liu, C. K., et al. (2026) · IEEE/ASME Transactions on Mechatronics · doi:10.1109/tmech.2025.3579247 · Cited by 130
Scarecrows in Oz: The Use of Large Language Models in HRI
Williams, T., Matuszek, C., Mead, R., & DePalma, N. (2024) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3606261 · Cited by 63
Yang, T., Feng, P., Guo, Q.-X., Zhang, J.-D., Zhang, X., Ning, J.-H., Wang, X., & Mao, Z. (2025) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3528892 · Cited by 49
A Survey of Reasoning with Foundation ModelsPreprint
Sun, J., Zheng, C., Xie, E., Liu, Z., Chu, R., Liu, J., Xu, J., Ding, M., Li, H., Geng, M., et al. (2023) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/ac4sp · Cited by 143
FlowMind: Automatic Workflow Generation with LLMs
Zeng, Z., Watson, W., Cho, N., Rahimi, S., Reynolds, S., Balch, T. R., & Veloso, M. (2023) · ACM International Conference on AI in Finance (ICAIF) · doi:10.1145/3604237.3626908 · Cited by 65
Prompt a Robot to Walk with Large Language Models
Wang, Y., Zhang, B., Chen, J., & Sreenath, K. (2024) · IEEE Conference on Decision and Control (CDC) · doi:10.1109/cdc56724.2024.10885862 · Cited by 86
Diffusion-ES: Gradient-Free Planning with Diffusion for Autonomous and Instruction-Guided Driving
Yang, B., Su, H., Gkanatsios, N., Ke, T.-W., Jain, A., Schneider, J., & Fragkiadaki, K. (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/cvpr52733.2024.01453 · Cited by 88
Object detection with multimodal large vision-language models: An in-depth review
Sapkota, R., & Karkee, M. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103575 · Cited by 61
Shi, W., Xu, R., Zhuang, Y., Yu, Y., Zhang, J., Wu, H., Zhu, Y., Ho, J. C., Yang, C., & Wang, M. D. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2024.emnlp-main.1245 · Cited by 54
“To mitigate ambiguities and misinterpretations in plan generation, an increasing number of LLM agents (Gao et al., 2023; Liang et al., 2023; Sun et al., 2023; Chen et al., 2023; Zhuang et al., 2024) employ code prompts as planner interface instead of natu-ral language prompts.”
BackgroundVision-Language Interpreter for Robot Task Planning
Shirai, K., Beltran-Hernandez, C. C., Hamaya, M., Hashimoto, A., Tanaka, S., Kawaharazuka, K., Tanaka, K., Ushiku, Y., & Mori, S. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611112 · Cited by 53
Robot learning in the era of foundation models: a survey
Xiao, X., Liu, J., Wang, Z., Zhou, Y., Qi, Y., Jiang, S., He, B., & Cheng, Q. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129963 · Cited by 56
Advancing real-time infectious disease forecasting using large language models
Du, H., Zhao, Y., Zhao, J., Xu, S., Lin, X., Chen, Y., Gardner, L., & Yang, H. (2025) · Nature Computational Science · doi:10.1038/s43588-025-00798-6 · Cited by 51
Raptis, E. K., Kapoutsis, A., & Kosmatopoulos, E. B. (2025) · Frontiers Robotics AI · doi:10.3389/frobt.2025.1605405 · Cited by 58
VoicePilot: Harnessing LLMs as Speech Interfaces for Physically Assistive Robots
Padmanabha, A., Yuan, J., Gupta, J., Karachiwalla, Z., Majidi, C., Admoni, H., & Erickson, Z. (2024) · ACM Symposium on User Interface Software and Technology (UIST) · doi:10.1145/3654777.3676401 · Cited by 45
Electromagnetic metamaterial agent
Hu, S., Li, M., Xu, J., Zhang, H., Zhang, S., Cui, T. J., del Hougne, P., & Li, L. (2025) · Light Science & Applications · doi:10.1038/s41377-024-01678-w · Cited by 50
Industrial Foundation Models (IFMs) for intelligent manufacturing: A systematic review
Zhao, S., Liu, S., Jiang, Y., Zhao, B. M., Lv, Y., Zhang, J., Wang, L., & Zhong, R. Y. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.06.011 · Cited by 50
RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation
Vecerík, M., Doersch, C., Yang, Y., Davchev, T., Aytar, Y., Zhou, G., Hadsell, R. T., Agapito, L., & Scholz, J. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10611409 · Cited by 44
Personalized Autonomous Driving with Large Language Models: Field Experiments
Cui, C., Yang, Z., Zhou, Y., Ma, Y., Lu, J., Li, L., Chen, Y., Panchal, J. H., & Wang, Z. (2024) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc58415.2024.10919978 · Cited by 44
Embodied Multi-Modal Agent trained by an LLM from a Parallel TextWorld
Yang, Y., Zhou, T., Li, K., Tao, D., Li, L., Shen, L., He, X., Jiang, J., & Shi, Y. (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/cvpr52733.2024.02482 · Cited by 51
Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models
Xiao, T. G., Chan, H., Sermanet, P., Wahid, A., Brohan, A., Hausman, K., Levine, S., & Tompson, J. (2023) · Robotics: Science and Systems · doi:10.15607/rss.2023.xix.029 · Cited by 84
Self-citationStatler: State-Maintaining Language Models for Embodied Reasoning
Yoneda, T., Fang, J., Li, P., Zhang, H., Jiang, T., Lin, S., Picker, B., Yunis, D., Mei, H., & Walter, M. R. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610634 · Cited by 62
Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning
Sun, J., Zhang, Q., Duan, Y., Jiang, X., Chen, C., & Xu, R. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610948 · Cited by 52
Driving Everywhere with Large Language Model Policy Adaptation
Li, B., Wang, Y., Mao, J., Ivanovic, B., Veer, S., Leung, K., & Pavone, M. (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/cvpr52733.2024.01416 · Cited by 41
Deploying and Evaluating LLMs to Program Service Mobile Robots
Hu, Z., Lucchetti, F., Schlesinger, C., Saxena, Y., Freeman, A., Modak, S., Guha, A., & Biswas, J. (2024) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2024.3360020 · Cited by 51
CoPAL: Corrective Planning of Robot Actions with Large Language Models
Joublin, F., Ceravola, A., Smirnov, P., Ocker, F., Deigmoeller, J., Belardinelli, A., Wang, C., Hasler, S., Tanneberg, D., & Gienger, M. (2024) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra57147.2024.10610434 · Cited by 65
Incremental learning of humanoid robot behavior from natural interaction and large language models
Bärmann, L., Kartmann, R., Peller-Konrad, F., Niehues, J., Waibel, A., & Asfour, T. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2024.1455375 · Cited by 46
SayCanPay: Heuristic Planning with Large Language Models Using Learnable Domain Knowledge
Hazra, R., Martires, P. Z. D., & Raedt, L. D. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i18.29991 · Cited by 51
BTGenBot: Behavior Tree Generation for Robotic Tasks with Lightweight LLMs
Izzo, R. A., Bardaro, G., & Matteucci, M. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802304 · Cited by 39
Visual Programming for Zero-Shot Open-Vocabulary 3D Visual Grounding
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Jailbreaking LLM-Controlled Robots
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DoReMi: Grounding Language Model by Detecting and Recovering from Plan-Execution Misalignment
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JARVIS -1: Open-World Multi-Task Agents With Memory-Augmented Multimodal Language Models
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StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects
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How People Prompt Generative AI to Create Interactive VR Scenes
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Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections
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Alchemist: LLM-Aided End-User Development of Robot Applications
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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 ›
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