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Title: Robot Behavior-Tree Task Generation with Large Language Models
Nowadays, the behavior tree is gaining popularity as a representation for robot tasks due to its modularity and reusability. Designing behavior-tree tasks manually is a time-consuming work for robot end- users, thus suggests a need for automatic behavior-tree task generation. Prior behavior-tree generation approaches focus on fixed primitive tasks and lack generalizability to new task domains. To cope with this issue, we propose a novel behavior-tree task generation approach with state-of-the-art large language models. We present a Phase-Step prompt design that enables hierarchical-structured robot task generation. We further integrate with behavior-tree-embedding-based search to set up the appropriate prompt. In such way, we enable automatic and cross-domain behavior-tree task generation. Our task generation approach does not require a set of pre-defined primitive tasks. End-user only needs to describe an abstract desired task and our approach can swiftly generate the corresponding behavior tree. Case studies are provided to demonstrate our approach.  more » « less
Award ID(s):
1813935
NSF-PAR ID:
10475173
Author(s) / Creator(s):
;
Publisher / Repository:
CEUR Workshop Proceedings
Date Published:
Journal Name:
CEUR workshop proceedings
ISSN:
1613-0073
Subject(s) / Keyword(s):
["AI","Robotics","Assembly Plan"]
Format(s):
Medium: X
Location:
San Francisco, CA, U.S.A.
Sponsoring Org:
National Science Foundation
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