title: Interactive Task Planning with Language Models
summary: 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.
paper_authors._id: 652e0f3e1a8fed0811d3b0f4
paper_authors.name: Boyi Li
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-10-18T08:06:54.277Z
paper_authors._id: 620dd3888528f797e88cb9b5
paper_authors.avatarUrl: /avatars/af04728788d78fe7d6375e19e32a535e.svg
paper_authors.fullname: Boyi Li
paper_authors.type: user
paper_authors.user: Boyiliee
paper_authors._id: 652e0f3e1a8fed0811d3b0f5
paper_authors.name: Philipp Wu
paper_authors._id: 652e0f3e1a8fed0811d3b0f6
paper_authors.name: Pieter Abbeel
paper_authors._id: 652e0f3e1a8fed0811d3b0f7
paper_authors.name: Jitendra Malik
paper_title: Interactive Task Planning with Language Models
paper_summary: 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.
paper_ai_keywords: language models high-level planning low-level function execution prompt engineering domain-specific pretrained models task guidelines replan accordingly