使用语言模型进行交互式任务规划

摘要

一个交互式机器人框架实现了长期任务规划，并且能够在执行过程中轻松泛化到新目标或不同任务，然而，大多数传统方法需要预定义的模块设计，这使得很难泛化到不同的目标。最近基于大型语言模型的方法可以实现更加开放式的规划，但通常需要大量提示工程或领域特定的预训练模型。为了解决这个问题，我们提出了一个简单的框架，通过语言模型实现交互式任务规划。我们的系统通过语言同时整合了高层规划和低层功能执行。我们验证了我们的系统在为未见目标生成新颖的高层指令方面的稳健性，以及通过仅替换任务指南而无需额外复杂的提示工程就能轻松适应不同任务的能力。此外，当用户发送新请求时，我们的系统能够根据新请求、任务指南和先前执行的步骤精确地重新规划。请查看我们的更多详细信息：https://wuphilipp.github.io/itp_site 和 https://youtu.be/TrKLuyv26_g。

English

An interactive robot framework accomplishes long-horizon task planning and can easily generalize to new goals or distinct tasks, even during execution. However, most traditional methods require predefined module design, which makes it hard to generalize to different goals. Recent large language model based approaches can allow for more open-ended planning but often require heavy prompt engineering or domain-specific pretrained models. To tackle this, we propose a simple framework that achieves interactive task planning with language models. Our system incorporates both high-level planning and low-level function execution via language. We verify the robustness of our system in generating novel high-level instructions for unseen objectives and its ease of adaptation to different tasks by merely substituting the task guidelines, without the need for additional complex prompt engineering. Furthermore, when the user sends a new request, our system is able to replan accordingly with precision based on the new request, task guidelines and previously executed steps. Please check more details on our https://wuphilipp.github.io/itp_site and https://youtu.be/TrKLuyv26_g.

使用语言模型进行交互式任务规划

Interactive Task Planning with Language Models

摘要

Support