Cocobo: Exploring Large Language Models as the Engine for End-User Robot Programming
Not on the map. Why?
Abstract (arXiv)
End-user development allows everyday users to tailor service robots or applications to their needs. One user-friendly approach is natural language programming. However, it encounters challenges such as an expansive user expression space and limited support for debugging and editing, which restrict its application in end-user programming. The emergence of large language models (LLMs) offers promising avenues for the translation and interpretation between human language instructions and the code executed by robots, but their application in end-user programming systems requires further study. We introduce Cocobo, a natural language programming system with interactive diagrams powered by LLMs. Cocobo employs LLMs to understand users' authoring intentions, generate and explain robot programs, and facilitate the conversion between executable code and flowchart representations. Our user study shows that Cocobo has a low learning curve, enabling even users with zero coding experience to customize robot programs successfully.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 50 references that are in the map. First posted on arXiv 30 Jul 2024.
Builds on
15 of 50 references.
References · 15
- 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
- 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
- 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
- 2022Emergent Abilities of Large Language Modelscited by 2,483
- 2023LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attentioncited by 776
- 2023ChatGPT for Robotics: Design Principles and Model Abilitiescited by 296
- 2021AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model Promptscited by 265
- 2023The Programmer's Assistant: Conversational Interaction with a Large Language Model for Software Developmentcited by 95
- 2023Graphologue: Exploring Large Language Model Responses with Interactive Diagramscited by 65
- 2023"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Modelscited by 48
- 2021In-IDE Code Generation from Natural Language: Promise and Challengescited by 41
- 2022What is it like to program with artificial intelligence?cited by 39
3 more
- 2021A Survey on End-User Robot Programmingcited by 5
- 2018Evaluating Methods for End-User Creation of Robot Task Planscited by 4
- 2023Sketching Robot Programs On the Flycited by 4
Built on by
Citing papers per year: 2025: 4; 2026: 1.
5 papers.
- 2025AutoMisty: A Multi-Agent LLM Framework for Automated Code Generation in the Misty Social Robotcited by 5
- 2025GenComUI: Exploring Generative Visual Aids as Medium to Support Task-Oriented Human-Robot Communicationcited by 1
- 2025Bootstrapping Human-Like Planning via LLMscited by 0
- 2025Feasibility of AI-Assisted Programming for End-User Developmentcited by 0
- 2026SkillComposer: Learning Reusable Skills for Natural-Language Robot Programmingcited by 0
- 2026SkillComposer: Learning Reusable Skills for Natural-Language Robot Programming14 Aug 2026
- 2025Feasibility of AI-Assisted Programming for End-User Development5 Dec 2025
- 2025Bootstrapping Human-Like Planning via LLMs27 Jun 2025
- 2025AutoMisty: A Multi-Agent LLM Framework for Automated Code Generation in the Misty Social Robot9 Mar 2025
- 2025GenComUI: Exploring Generative Visual Aids as Medium to Support Task-Oriented Human-Robot Communication15 Feb 2025