Interactive Task Planning with Language Models
Boyi Li Philipp Wu Pieter Abbeel Jitendra Malik
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.