使用大型语言模型进行神经象征性机器人行动计划的框架
Alessio Capitanelli1, Fulvio Mastrogiovanni1
1Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.
Frontiers in neurorobotics
|June 19, 2024
概括
本研究介绍了Teriyaki,这是一个使用大型语言模型 (LLM) 进行机器人任务规划的新框架. 泰里亚基在动态环境中提高了可扩展性和效率,改善了人机交互.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 象征性任务规划对于机器人的自主性至关重要,但在动态的人机协作场景中,它在可扩展性方面存在困难.
- 限制包括缓慢的计划时间,长时间的计划长度,以及难以适应现实世界的变化,阻碍机器人的效率和交互流性.
研究的目的:
- 通过使用大型语言模型 (LLM) 开发神经符号任务计划器,弥合象征性任务规划和机器学习之间的差距.
- 利用LLM的生成能力来克服复杂和动态环境中的传统符号规划者的局限性.
主要方法:
- 开发了一个名为Teriyaki的框架,将GPT-3 (大型语言模型) 训练成与PDDL (规划域定义语言) 兼容的神经符号任务规划器.
- 利用了LLM的线性扩展响应时间和逐步行动计划综合,用于并发的规划和执行.
主要成果:
- 在1000个样本的测试数据集上,Teriyaki实现了95.5%的解决问题率.
- 生成的计划比传统的象征性规划器要短13.5%.
- 计划可用性的平均等待时间减少了61.4%.
结论:
- 泰里亚基的规划性能与传统规划人员相美,同时通过LLM集成提高了可扩展性和效率.
- 该框架允许更快,更适应的机器人任务规划,在复杂的场景中改善人机交互.
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