ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter
arXiv:2407.11298v2 Announce Type: replace Abstract: 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
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Abstract: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.
Comments: Accepted at CoRL 2024. Project Website:(this https URL)
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2407.11298 [cs.RO]
(or arXiv:2407.11298v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2407.11298
arXiv-issued DOI via DataCite
Submission history
From: Yaoyao Qian [view email] [v1] Tue, 16 Jul 2024 01:06:33 UTC (15,066 KB) [v2] Thu, 2 Apr 2026 13:26:45 UTC (15,072 KB)
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