用大模型将自然语言转为人类级机器人动作序列
Bootstrapping Human-Like Planning via LLMs
- 大模型输入自然语言，输出人类级别动作序列
- 大模型生成序列与人工标注序列相似度更高
- 小模型也能达到可用水平，适合资源受限场景
机器人用户越来越需要简便的任务指定方式。常见两种方式为拖拽界面和自然语言编程。虽然自然语言接口利用了直观的人类沟通方式，但拖拽界面能更精确地定义机器人的关键动作。本文研究如何结合二者：构建基于大语言模型（LLM）的流水线，输入自然语言，输出人类级别的动作序列，粒度与人类手写一致。随后将生成序列与人工标注的动作序列数据集对比。结果显示，大模型在生成人类级动作序列方面表现优于小模型，但小模型仍能达到满意效果。
原文摘要 · Abstract (English)
Robot end users increasingly require accessible means of specifying tasks for robots to perform. Two common end-user programming paradigms include drag-and-drop interfaces and natural language programming. Although natural language interfaces harness an intuitive form of human communication, drag-and-drop interfaces enable users to meticulously and precisely dictate the key actions of the robot's task. In this paper, we investigate the degree to which both approaches can be combined. Specifically, we construct a large language model (LLM)-based pipeline that accepts natural language as input and produces human-like action sequences as output, specified at a level of granularity that a human would produce. We then compare these generated action sequences to another dataset of hand-specified action sequences. Although our results reveal that larger models tend to outperform smaller ones in the production of human-like action sequences, smaller models nonetheless achieve satisfactory performance.
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