利用語言模型進行互動式任務規劃

摘要

一個互動式機器人框架實現了長期規劃任務，並可以在執行過程中輕鬆泛化到新目標或不同任務。然而，大多數傳統方法需要預定義的模塊設計，這使得很難泛化到不同的目標。最近基於大型語言模型的方法可以實現更開放式的規劃，但通常需要大量提示工程或特定領域的預訓練模型。為了應對這一問題，我們提出了一個簡單的框架，可以利用語言模型實現互動式任務規劃。我們的系統通過語言同時整合了高層規劃和低層功能執行。我們驗證了我們系統生成新的高層指令以應對未知目標的魯棒性，以及通過僅替換任務指南而無需額外複雜提示工程來適應不同任務的便利性。此外，當用戶發送新請求時，我們的系統能夠根據新請求、任務指南和先前執行的步驟精確重新規劃。請查看我們的更多詳細信息，請訪問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.

利用語言模型進行互動式任務規劃

Interactive Task Planning with Language Models

摘要

Support