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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
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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
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.
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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
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.
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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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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
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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]”
From Code as Agent Harness (2026) · cites this paper as [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].”
From Voyager: An Open-Ended Embodied Agent with Large Language Models (2023) · cites this paper as [16]
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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].”
From Code as Agent Harness (2026) · cites this paper as [10]
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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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Citations
Cites
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PaLM: Scaling Language Modeling with Pathways
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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
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Evaluating Large Language Models Trained on Code
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- kalashnikov, 2018, Scalable deep reinforcement learning for vision-based robotic manipulation, CoRL
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Ma, S., Li, J., Zhang, G., Yu, J., Li, W., & Ma, X. (2026) · World Conference on Computer and Information Security (WCCIS) · doi:10.1109/wccis70285.2026.11650912
Liu, C.-C., Vu, C.-T., & Liu, Y. (2026) · IEEE/ASME International Conference on Advanced Intelligent Mechatronics · doi:10.1109/aim65483.2026.11658058
Robot Independent Intelligence: When Language Vanishes in Vision-Language-Action Models
Gui, K., Gui, S., & Luximon, Y. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3728316
GAS-Robo: Converting Scene Into a Grid-Action Space for LLM-Driven Open-Ended Robotic Manipulation
Li, H., Xie, S., Lin, Z., Xiao, Z., Wang, R., & Chen, X. (2026) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2026.3728325
Internet of Agents (IoA): Sensing, Communication, Decision-Making, and Execution
Wang, J., Ning, H., Ding, J., Zhu, T., & Daneshmand, M. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3729267
Lin, L., Shi, M., & Wang, T. (2026) · AI · doi:10.3390/ai7090334
Large language model-based primitive-structured language-geometry fusion for robotic manipulationPreprint
Deng, X., Liu, J., Fan, M., Cao, Y., He, C., Zhang, Y., & Sartoretti, G. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7389353
MPPI Cost Function Design for Quadrupedal Locomotion via Hierarchical LLM-CEM Optimization
Tao, C., Wu, H., Wang, F., Jiang, H., Li, Z., & Gao, Y. (2026) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/cis-ram71614.2026.11668488
A Few-Shot VLM-Driven Perception-to-Action Pipeline for Robotic Pick-and-Place
You, S., Xue, J., Tunell, J. N., Costa, G. D. D., Hao, X., Wang, J., & Liu, G. (2026) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/cis-ram71614.2026.11668536
Control semántico de un robot rehabilitador mediante modelos de lenguaje
Sorozábal, P., Varela, I. D., Sendra-Arranz, R., Valverde-García, J. M., Laudanski, A. F., & Rocón, E. (2026) · Jornadas de Automática · doi:10.17979/ja-cea.2026.47.13823
LLM-Based Hierarchical Control Architecture for Robotic Operation
Ma, M. (2026) · IEEE Conference on Cybernetics and Intelligent Systems · doi:10.1109/cis-ram71614.2026.11668448
Khan, O. A., & Ghaffari, A. (2026) · Research Square · doi:10.21203/rs.3.rs-10643317/v1
Ma, G., Guo, J., Tang, X., Zhou, H., & Rong, Y. (2026) · Sensors · doi:10.3390/s26175595
Constrained Behavior Tree Generation for Safe LLM-Driven Robot Navigation
Perando, J., & Pan, X. (2026) · IEEE International Conference on Electro Information Technology · doi:10.1109/eit68936.2026.11670368
Federated Self-Evolving Embodied AI Agents
Shen, L., & Zheng, Y. (2026) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infocom59046.2026.11680069
ZAP: Zero-Shot Assembly Planning with Vision-Language Models
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Towards Socially Intelligent Robots: Research on Human-Aware Vision Language Navigation
Chen, J. (2026) · Exploring Science Academic Conference Series · doi:10.70267/aitia.2026216222
A reasoning-LLM-based high-level planning framework for robotic shelf-stocking and disposal tasks
Ishiyama, Y., Onishi, M., Sakazaki, H., & Demura, K. (2026) · Advanced Robotics · doi:10.1080/01691864.2026.2728199
High-Level Semantic Instruction-Guided UAV Area Search Using LLM Agents
Fu, Y., Zhang, S., Zhao, J., Liu, B., & Liu, Y. (2026) · International Conference on Big Data and Information Analytics (BigDIA) · doi:10.1109/bigdia71496.2026.11684614
Zhang, X. (2026) · International Symposium on Control Engineering and Robotics (ISCER) · doi:10.1109/iscer70245.2026.11681813
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Pseudocode as a Stored Strategy Representation for LLM-Based Artificial Market Simulation
Itakura, R., Hirano, M., Imajo, K., Sakaji, H., & Noda, I. (2026) · IEEE Conference on Computational Intelligence for Financial Engineering & Economics · doi:10.1109/cifer67845.2026.11692404
SMaRTAban: a voice-controlled LLM agent for quadruped mobile robotics with integrated vision
Zelenay, E., Kocúr, M., Lukáč, M., Duchoň, F., & Marko, R. (2026) · International Journal of Intelligent Robotics and Applications · doi:10.1007/s41315-026-00593-0
Kim, D., & Jung, H. (2026) · Biomimetics · doi:10.3390/biomimetics11090669
Muntean, E., Leba, M., & Ionica, A. C. (2026) · Biomimetics · doi:10.3390/biomimetics11090670
Zuzuárregui, M. A., Abedi, A. A., Ehsani, R. J., & Carpin, S. (2026) · IEEE transactions on field robotics. · doi:10.1109/tfr.2026.3736546
VigiClaw: Action-Triggered State Verification for Robust Long-Horizon Robotic Manipulation
Lu, P., Kang, N., Gao, C., Wang, Y., & Lu, Z. (2026) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber71431.2026.11695509
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.
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