Cocobo: Exploring Large Language Models as the Engine for End-User Robot Programming

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.

Authors

Institutions

Publication Details

Published
2024-09-02
DOI
https://doi.org/10.1109/vl/hcc60511.2024.00020
Citations
4
Primary Topic
Reinforcement Learning in Robotics
Type
article
Field-Weighted Citation Impact
1.39
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Cocobo: Exploring Large Language Models as the Engine for End-User Robot Programming

Xiaohua Sun, 潤爾 大山, Yuanda Hu, Yate Ge et al.
4 citations
Reinforcement Learning in Robotics
1.39
article

Cocobo: Exploring Large Language Models as the Engine for End-User Robot Programming

Xiaohua Sun, 潤爾 大山, Yuanda Hu, Yate Ge, Kechun Li, Yi Dai
article en
4 citations

Abstract

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.

Vol. 4
Tongji University (CN), Southern University of Science and Technology (CN)
Openalex Percentile: Top 15%
Reinforcement Learning in Robotics
1.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.