用GPT-4让机器人实时执行复杂任务，稳定且安全。
Exploring GPT-4 for Robotic Agent Strategy with Real-Time State Feedback and a Reactive Behaviour Framework
- 用GPT-4生成可执行子任务，结合实时状态反馈与行为框架
- 在模拟和真实机器人上实现100%可执行性，多数任务成功完成
- 适合需要动态响应与安全执行的机器人应用开发
我们探索了在仿真环境和真实世界中使用GPT-4驱动类人机器人行为的可行性，验证了一种新型大语言模型（LLM）驱动的行为方法。尽管已有研究关注LLM生成任务的可执行性与正确性，但本工作聚焦于实际部署中的安全性、任务间过渡、时间跨度及状态反馈等关键问题。实验表明，该方法能始终生成可执行请求，确保平滑的任务切换，并在多种目标时间范围内实现用户请求的高成功率。
原文摘要 · Abstract (English)
We explore the use of GPT-4 on a humanoid robot in simulation and the real world as proof of concept of a novel large language model (LLM) driven behaviour method. LLMs have shown the ability to perform various tasks, including robotic agent behaviour. The problem involves prompting the LLM with a goal, and the LLM outputs the sub-tasks to complete to achieve that goal. Previous works focus on the executability and correctness of the LLM's generated tasks. We propose a method that successfully addresses practical concerns around safety, transitions between tasks, time horizons of tasks and state feedback. In our experiments we have found that our approach produces output for feasible requests that can be executed every time, with smooth transitions. User requests are achieved most of the time across a range of goal time horizons.
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