ThinkGrasp is a vision-language system designed for strategic part grasping in cluttered environments. It leverages GPT-4o's advanced contextual reasoning to identify and generate grasp poses for target objects, even when they are heavily obstructed or nearly invisible. The system uses goal-oriented language to guide the removal of obstructing objects, progressively uncovering the target and ultimately grasping it with high success rates. In both simulated and real-world experiments, ThinkGrasp outperforms state-of-the-art methods in heavily cluttered environments and diverse object scenarios, demonstrating strong generalization capabilities. The system combines large-scale pretrained vision-language models with an occlusion handling system. It uses GPT-4o to understand environmental and object properties, then integrates this knowledge through a structured prompt-based chain of thought to enhance success rates and ensure safe grasp poses. ThinkGrasp prioritizes larger, centrally located objects to maximize visibility and access, focusing on safe and advantageous parts like handles or flat surfaces. It uses LangSAM and VLPart for segmentation, ensuring that errors from the language model do not affect the segmentation process. In simulation, ThinkGrasp achieves a 98.0% success rate with fewer steps, outperforming prior methods like OVGNet (43.8%) and VLG (75.3%). In real-world settings, it achieves high success rates with few steps. The system's modular design allows easy integration into various robotic platforms and grasping systems, compatible with 6-DoF two-finger grippers. It quickly adapts to new language goals and novel objects through simple prompts, making it highly versatile and scalable. ThinkGrasp uses a 3×3 grid strategy to select optimal grasp regions, improving robustness in low-resolution images. It generates candidate grasp poses based on point cloud data, evaluates them based on proximity to preferred locations and grasp quality scores, and selects the optimal pose. The system employs a closed-loop process, continuously updating scene understanding after each grasp attempt to ensure the most current information is used. In experiments, ThinkGrasp outperforms baselines in success rates and efficiency metrics, achieving an average success rate of 0.980 with an average step count of 3.39 in clutter cases. It also performs well in heavy clutter scenarios, achieving high success rates despite the presence of unseen objects and heavy clutter. Ablation studies show that integrating GPT-4o with LangSAM and VLPart significantly improves performance, demonstrating the importance of each component in enhancing overall effectiveness. In real-world experiments, ThinkGrasp successfully identifies and grasps target objects in cluttered environments, with the integration of VLPart and GPT-4o enhancing robustness and accuracy. However, some failures occurred due to limitations of single-view point cloud reconstruction, low-quality grasp poses, and variations in robotic stability and control. Future work aims to address these limitations by incorporating multi-view point cloud integration and expanding the rangeThinkGrasp is a vision-language system designed for strategic part grasping in cluttered environments. It leverages GPT-4o's advanced contextual reasoning to identify and generate grasp poses for target objects, even when they are heavily obstructed or nearly invisible. The system uses goal-oriented language to guide the removal of obstructing objects, progressively uncovering the target and ultimately grasping it with high success rates. In both simulated and real-world experiments, ThinkGrasp outperforms state-of-the-art methods in heavily cluttered environments and diverse object scenarios, demonstrating strong generalization capabilities. The system combines large-scale pretrained vision-language models with an occlusion handling system. It uses GPT-4o to understand environmental and object properties, then integrates this knowledge through a structured prompt-based chain of thought to enhance success rates and ensure safe grasp poses. ThinkGrasp prioritizes larger, centrally located objects to maximize visibility and access, focusing on safe and advantageous parts like handles or flat surfaces. It uses LangSAM and VLPart for segmentation, ensuring that errors from the language model do not affect the segmentation process. In simulation, ThinkGrasp achieves a 98.0% success rate with fewer steps, outperforming prior methods like OVGNet (43.8%) and VLG (75.3%). In real-world settings, it achieves high success rates with few steps. The system's modular design allows easy integration into various robotic platforms and grasping systems, compatible with 6-DoF two-finger grippers. It quickly adapts to new language goals and novel objects through simple prompts, making it highly versatile and scalable. ThinkGrasp uses a 3×3 grid strategy to select optimal grasp regions, improving robustness in low-resolution images. It generates candidate grasp poses based on point cloud data, evaluates them based on proximity to preferred locations and grasp quality scores, and selects the optimal pose. The system employs a closed-loop process, continuously updating scene understanding after each grasp attempt to ensure the most current information is used. In experiments, ThinkGrasp outperforms baselines in success rates and efficiency metrics, achieving an average success rate of 0.980 with an average step count of 3.39 in clutter cases. It also performs well in heavy clutter scenarios, achieving high success rates despite the presence of unseen objects and heavy clutter. Ablation studies show that integrating GPT-4o with LangSAM and VLPart significantly improves performance, demonstrating the importance of each component in enhancing overall effectiveness. In real-world experiments, ThinkGrasp successfully identifies and grasps target objects in cluttered environments, with the integration of VLPart and GPT-4o enhancing robustness and accuracy. However, some failures occurred due to limitations of single-view point cloud reconstruction, low-quality grasp poses, and variations in robotic stability and control. Future work aims to address these limitations by incorporating multi-view point cloud integration and expanding the range