ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter
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.
Community
โ How do you solve grasping problems when your target object is completely out of sight?
๐ Excited to share our latest research! Check out ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter.
๐ Site: http://h-freax.github.io/thinkgrasp_page
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- Towards Open-World Grasping with Large Vision-Language Models (2024)
- Manipulate-Anything: Automating Real-World Robots using Vision-Language Models (2024)
- Open-Vocabulary Part-Based Grasping (2024)
- Navi2Gaze: Leveraging Foundation Models for Navigation and Target Gazing (2024)
- RoboPoint: A Vision-Language Model for Spatial Affordance Prediction for Robotics (2024)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 0
No model linking this paper
Datasets citing this paper 0
No dataset linking this paper
Spaces citing this paper 0
No Space linking this paper