title: ThinkGrasp: A Vision-Language System for Strategic Part Grasping in
    Clutter
summary: Robotic grasping in cluttered environments remains a significant challenge
  due to occlusions and complex object arrangements. We have developed
  ThinkGrasp, a plug-and-play vision-language grasping system that makes use of
  GPT-4o's advanced contextual reasoning for heavy clutter environment grasping
  strategies. ThinkGrasp can effectively identify and generate grasp poses for
  target objects, even when they are heavily obstructed or nearly invisible, by
  using goal-oriented language to guide the removal of obstructing objects. This
  approach progressively uncovers the target object and ultimately grasps it with
  a few steps and a high success rate. In both simulated and real experiments,
  ThinkGrasp achieved a high success rate and significantly outperformed
  state-of-the-art methods in heavily cluttered environments or with diverse
  unseen objects, demonstrating strong generalization capabilities.
paper_authors._id: 669843f1615c05521cae4c2f
paper_authors.name: Yaoyao Qian
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-07-18T16:47:31.207Z
paper_authors._id: 65c431a609672feb8cac22e7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65c431a609672feb8cac22e7/obXCXUOgEoCuJbGuzYOGA.jpeg
paper_authors.fullname: Yaoyao Qian
paper_authors.type: user
paper_authors.user: FreaxRuby
paper_authors._id: 669843f1615c05521cae4c30
paper_authors.name: Xupeng Zhu
paper_authors._id: 669843f1615c05521cae4c31
paper_authors.name: Ondrej Biza
paper_authors._id: 669843f1615c05521cae4c32
paper_authors.name: Shuo Jiang
paper_authors._id: 669843f1615c05521cae4c33
paper_authors.name: Linfeng Zhao
paper_authors._id: 669843f1615c05521cae4c34
paper_authors.name: Haojie Huang
paper_authors._id: 669843f1615c05521cae4c35
paper_authors.name: Yu Qi
paper_authors._id: 669843f1615c05521cae4c36
paper_authors.name: Robert Platt
paper_title: ThinkGrasp: A Vision-Language System for Strategic Part Grasping in
    Clutter
paper_summary: Robotic grasping in cluttered environments remains a significant challenge
  due to occlusions and complex object arrangements. We have developed
  ThinkGrasp, a plug-and-play vision-language grasping system that makes use of
  GPT-4o's advanced contextual reasoning for heavy clutter environment grasping
  strategies. ThinkGrasp can effectively identify and generate grasp poses for
  target objects, even when they are heavily obstructed or nearly invisible, by
  using goal-oriented language to guide the removal of obstructing objects. This
  approach progressively uncovers the target object and ultimately grasps it with
  a few steps and a high success rate. In both simulated and real experiments,
  ThinkGrasp achieved a high success rate and significantly outperformed
  state-of-the-art methods in heavily cluttered environments or with diverse
  unseen objects, demonstrating strong generalization capabilities.
paper_ai_keywords: GPT-4o vision-language grasping system contextual reasoning load clutter environment grasping strategies grasp poses goal-oriented language generalization capabilities