ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
Open accessAbstract
Task planning can require defining myriad domain knowledge about the world in which a robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to score potential next actions during task planning, and even generate action sequences directly, given an instruction in natural language with no additional domain information. However, such methods either require enumerating all possible next steps for scoring, or generate free-form text that may contain actions not possible on a given robot in its current context. We present a programmatic LLM prompt structure that enables plan generation functional across situated environments, robot capabilities, and tasks. Our key insight is to prompt the LLM with program-like specifications of the available actions and objects in an environment, as well as with example programs that can be executed. We make concrete recommendations about prompt structure and generation constraints through ablation experiments, demonstrate state of the art success rates in VirtualHome household tasks, and deploy our method on a physical robot arm for tabletop tasks. Website at progprompt.github.io
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VLMs can provide powerful visual semantic understanding and useful guidance to robotic tasks.
“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 [10]
The Real-Robot Experiments section describes using an open-vocabulary object detector to identify objects in a scene, then an LLM to plan pick-and-place actions. This supports visual information being useful to a robotic task, but the paper attributes task planning to an LLM; it does not establish the broader claim that VLMs themselves provide the described guidance or that they are trained on large image-and-language datasets. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSeveral works have shown that language models are capable planners for robotic tasks.
“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 [10]
Programs can naturally represent temporally extended and compositional actions, which are essential for many long-horizon tasks in Minecraft.
“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 [22]
Recent LLM-based agents harness world knowledge to generate consistent action plans or executable policies.
“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 [22]
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Self-citation
- Authors
- Singh, I., Blukis, V., Mousavian, A., Goyal, A., Xu, D., Tremblay, J., Fox, D., Thomason, J., & Garg, A.
- Affiliations
- University of Southern California (US)
- Nvidia (United Kingdom) (GB)
- Venue
- 2023 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2023-05-29
- Pages
- 11523-11530
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W4383108457
- Cited by
- 613
- References
- 10
- Citations by year
- 2026: 96 · 2025: 260 · 2024: 208 · 2023: 46 · 2022: 3
- FWCI
- 108.08
- 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 › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Engineering › Control and Systems Engineering › Robot Manipulation and Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- robot task planning, large language models, action sequence generation, robot capabilities, housework, robot manipulators, tabletop manipulation, prompt structure
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