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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
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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“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]
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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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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]
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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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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]
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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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Abstraction Refinement-Guided Program Synthesis for Robot Learning from Demonstrations
Cui, G., Wang, Y., Mao, W., Duan, Y., & Zhu, H. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3763070 · Cited by 1
Sun, S., Liu, R., Lyu, J., Yang, J.-W., Zhang, L., & Li, X. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5166087 · Cited by 2
Teodorescu, L. (2023) · theses.fr (ABES) · doi:10.70675/08e0bf33z987fz4ff6zbdd7zbf6cf87e81f0 · Cited by 1
Evaluating LLMs in ROS robotic software code generation
Zhao, Y., Mao, X., Zhang, T., Li, T., & Xiao, Z. (2027) · Empirical Software Engineering · doi:10.1007/s10664-026-10910-7
Back into Plato's Cave: Examining Cross-modal Representational Convergence at Scale
Koepke, A. S., Zverev, D., Ginosar, S., & Efros, A. A. (2026) · arXiv · doi:10.48550/arxiv.2604.18572
“Several works have demonstrated that models trained only on text data successfully solve what were thought to be fundamentally visual problems, such as visual question answering (VQA) [32, 40], visual reasoning [11, 39, 98, 2], or embodied robotics applications [1, 57].”
AutoHarness: improving LLM agents by automatically synthesizing a code harness
Lou, X., Lázaro-Gredilla, M., Dedieu, A., Wendelken, C., Lehrach, W., & Murphy, K. P. (2026) · arXiv · doi:10.48550/arxiv.2603.03329
“…rejection sampling loop where we only learn a conditioning function with signature def is_legal_action() ), or less so, with maximum flexibility resulting in a code-as-policy setup (Liang et al., 2023) in which code proposes the next action directly and no LLM calls are needed at execution time.”
Ning, X., Tieu, K., Fu, D., Wei, T., Li, Z., Bei, Y., Zou, J., Ai, M., Liu, Z., Li, T.-W., et al. (2026) · arXiv · doi:10.48550/arxiv.2605.18747
Design and Implementation of a Multimodal Combination Framework for Robotic Grasping
Huang, C., Wang, Z., Zhu, H., Li, J., & Liu, X. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43111-1_2
Symbolic Planning and Code Generation for Grounded Dialogue
Chiu, J., Zhao, W., Chen, D., Vaduguru, S., Rush, A. M., & Fried, D. (2023) · doi:10.18653/v1/2023.pandl-1.5
Editorial: Language, affordance and physics in robot cognition and intelligent systems
Chen, N., Mayol-Cuevas, W. W., Karl, M., Aljalbout, E., Zeng, A., Cortese, A., Burgard, W., & van Hoof, H. (2024) · Frontiers in Robotics and AI · doi:10.3389/frobt.2023.1355576
Self-citationAction2Code: Transforming Video Demonstrations into Sequential Robotic Instructions
Upadhyay, A., Reddy, G., Dubey, A., Saurav, S. K., & Sengupta, S. (2024) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur61395.2024.10597493
Agent Can Say No: Robot Task Planning by Natural Language Feedback Between Planner and Executor
Zhao, X., Jing, M., & Wu, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5675-9_13 · Cited by 1
Guan, I. Y. N., Zhang, G., Liu, X., Zhao, E., & Wu, J. (2024) · International Conference on Electrical Engineering, Control and Robotics (EECR) · doi:10.1109/eecr60807.2024.10607324
Suzuki, T., & Hashimoto, M. (2024) · Journal of the Japan Society for Precision Engineering · doi:10.2493/jjspe.90.859
LLaKey: Follow My Basic Action Instructions to Your Next Key State
Zhao, Z., He, Y., Yu, F. R., Li, P., Zhuo, F., & Sun, X. (2024) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros58592.2024.10802579
The Role of Large Language Models in Evolving Digital Landscapes: A Subject-Oriented Analysis
Greif, L., & Elstermann, M. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-8598-8_11
Kinder, L., & Käfer, T. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78955-7_21
On the Potential of Large Language Models in Parallel Systems: A Systematic Analysis
Cheng, X., Luo, S., Yang, J., Lei, Y., Li, Z., & Yang, L. (2024) · China Automation Congress (CAC) · doi:10.1109/cac63892.2024.10864500
Reward Design Framework Based on Reward Components and Large Language Models
Jin, K., Tian, G., Huang, B., Cui, Y., & Zheng, X. (2024) · International Conference on Artificial Intelligence, Robotics, and Communication (ICAIRC) · doi:10.1109/icairc64177.2024.10899926
MVP-MCTS: Language Modelling a Flexible Multi-Agent Planning Framework
Li, Z., & Ji, J. (2024) · International Symposium on Artificial Intelligence and Intelligent Manufacturing (AIIM) · doi:10.1109/aiim64537.2024.10934217
Wu, O. T., Chan, F. K. S., & Law, Y. N. (2024) · International Conference on Computers and Artificial Intelligence Technology (CAIT) · doi:10.1109/cait64506.2024.10963264
Imran, A., & Gopalakrishnan, K. (2025) · Apress eBooks · doi:10.1007/979-8-8688-0989-7_4
Challenges in Developing Robotic Systems Based on Large Language Models
Li, M., Pang, J., & Cen, F. (2024) · International Journal of Artificial Intelligence and Robotics Research · doi:10.1142/s297233532501001x
CORA: A Chain of Robotic Actions Reasoning Model for Autonomous Robotic Arm Manipulation
Yu, H., Yang, F., & Fang, N. (2025) · International Conference on Automation, Robotics and Applications · doi:10.1109/icara64554.2025.10977668
Flexible voice control for collaborative robotics
Kötter, D., Teknetas, D., Kolditz, M., Petrović, O., & Brecher, C. (2025) · at - Automatisierungstechnik · doi:10.1515/auto-2025-0008
Enhancing Product Search Interfaces with Sketch-Guided Diffusion and Language Agents
Sun, E. (2025) · International World Wide Web Conference (WWW) · doi:10.1145/3701716.3717853
A Proposal for Cooking Motion Generation Based on Maximizing the Margin of Task Achievement
Maruyama, K., Yamada, K., Suzuki, T., Akizuki, S., & Hashimoto, M. (2025) · International Journal of Automation Technology · doi:10.20965/ijat.2025.p0599
LLM-Assisted Plan Execution for Robots in Dynamic Environments
Peña‐Narvaez, J. D., Manzanares, J. C., Moreno, F. M., Pérez-Rodríguez, R., & Martín, F. (2026) · Robotics · doi:10.3390/robotics15040080
Foundation Models for Robotic Tasks: Survey, Challenges and Future DirectionsPreprint
Alam, M., Muthugala, M. A. V. J., & Elara, M. R. (2025) · doi:10.36227/techrxiv.175321822.20121795/v1
Image Transformation Sequence Retrieval with General Reinforcement Learning
Mas-Candela, E., Ríos-Vila, A., & Calvo-Zaragoza, J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-99565-1_26
SortingBot: Leveraging Large Language Models and 3D Vision for Multi-Category Material Sorting
Li, Y., Wang, H., & Fei, B. (2024) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon62851.2024.11093526
Zhu, X., Xie, J., Zhou, F., Meng, Y., Wang, C., & Nick, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5374357
Robocskbench: Benchmarking Embodied Commonsense Capabilities of Large Language Models
Töberg, J.-P., Kenneweg, S., & Cimiano, P. (2025) · International Conference on Ubiquitous Robots (UR) · doi:10.1109/ur65550.2025.11078036 · Cited by 1
Improving Zero-Shot ObjectNav with Generative Communication
Dorbala, V. S., Sharma, V. D., Tokekar, P., & Manocha, D. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127870
Leveraging Semantic and Geometric Information for Zero-Shot Robot-to-Human Handover
Liu, J., Dong, W., Wang, J., & Meng, M. Q. ‐. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11128058
Yang, J., Fu, C., Shah, D., Sadigh, D., Xia, F., & Zhang, T. (2025) · Robotics: Science and Systems · doi:10.15607/rss.2025.xxi.155 · Cited by 4
Self-citationLearning Machine Tending from Demonstration with Multimodal LLMs
Odabaşı, Ç., Lindermayr, J., Krieglstein, J., & Predrag, K. (2025) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case58245.2025.11163852
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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