学习在场景图表上推理:将GPT-2微调成机器人语言模型的案例研究,用于接地任务规划
Georgia Chalvatzaki1,2,3, Ali Younes1, Daljeet Nandha1
1Computer Science Department, Technische Universität Darmstadt, Darmstadt, Germany.
Frontiers in robotics and AI
|August 31, 2023
概括
这项研究表明,大型语言模型 (LLM) 可以有效地为机器人执行长期任务规划. 通过将LLM与场景图形接地,机器人可以将人类请求转化为可执行的计划.
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
- 自然语言处理自然语言处理.
背景情况:
- 长期的任务规划对于智能机器人来说至关重要.
- 现有的方法在复杂的,连续的任务执行中面临挑战.
- 大型语言模型 (LLM) 显示了推理和规划的潜力.
研究的目的:
- 研究GPT-2的使用,一种LLM类型,用于机器人任务规划.
- 开发一种方法,用机器人特定领域的场景图表来接地LLM输入.
- 评估基于LLM的规划人员与传统方法的表现.
主要方法:
- 利用GPT-2将长远的任务分解为连续的子目标.
- 在机器人的操作领域内使用场景图来接地LLM输入.
- 在ALFRED基准上将基于LLM的方法与经典的规划和基线技术进行了比较.
主要成果:
- 基于LLM的方法成功地将人类请求转化为可执行的机器人计划.
- 在长期任务上表现出有效的推理.
- 展示了基于LLM的规划的适用性和普遍性.
结论:
- 在LLM中的知识可以有效地为复杂的机器人任务规划奠定基础.
- 基于LLM的规划为机器人技术中的神经符号方法提供了一个有希望的方向.
- 这种方法提升了智能辅助和服务机器人的能力.
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