# ThinkGrasp: A Vision-Language System for Strategic Part Grasping in Clutter

arXiv: https://arxiv.org/abs/2407.11298 · HTML: https://mlsanity.com/paper/2407.11298/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2024.

## Built on by (most cited first, 20 of 20)

- [Diffusion Models for Robotic Manipulation: A Survey](https://mlsanity.com/paper/2504.08438/) (2025)
- [Multimodal Spatial Reasoning in the Large Model Era: A Survey and Benchmarks](https://mlsanity.com/paper/2510.25760/) (2025)
- [RoboMatrix: A Skill-centric Hierarchical Framework for Scalable Robot Task Planning and Execution in Open-World](https://mlsanity.com/paper/2412.00171/) (2024)
- [AffordGrasp: In-Context Affordance Reasoning for Open-Vocabulary Task-Oriented Grasping in Clutter](https://mlsanity.com/paper/2503.00778/) (2025)
- [VISO-Grasp: Vision-Language Informed Spatial Object-centric 6-DoF Active View Planning and Grasping in Clutter and Invisibility](https://mlsanity.com/paper/2503.12609/) (2025)
- [Free-form language-based robotic reasoning and grasping](https://mlsanity.com/paper/2503.13082/) (2025)
- [Dissecting Embodied Abilities in Multimodal Language Models through Skill-level Evaluation and Diagnosis](https://mlsanity.com/paper/2510.08759/) (2025)
- [Efficient Alignment of Unconditioned Action Prior for Language-conditioned Pick and Place in Clutter](https://mlsanity.com/paper/2503.09423/) (2025)
- [GraspCoT: Integrating Physical Property Reasoning for 6-DoF Grasping under Flexible Language Instructions](https://mlsanity.com/paper/2503.16013/) (2025)
- [VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generation](https://mlsanity.com/paper/2510.05827/) (2025)
- [UniDiffGrasp: A Unified Framework Integrating VLM Reasoning and VLM-Guided Part Diffusion for Open-Vocabulary Constrained Grasping with Dual Arms](https://mlsanity.com/paper/2505.06832/) (2025)
- [A Pragmatist Robot: Learning to Plan Tasks by Experiencing the Real World](https://mlsanity.com/paper/2507.16713/) (2025)
- [Obstruction reasoning for robotic grasping](https://mlsanity.com/paper/2511.23186/) (2025)
- [RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation](https://mlsanity.com/paper/2603.14880/) (2026)
- [Adaptive Vision-Language Grasping via Composable Foundation Priors and Generalizable Grasp Synthesis](https://mlsanity.com/paper/2609.04096/) (2026)
- [Attribute-based Object Grounding and Robot Grasp Detection with Spatial Reasoning](https://mlsanity.com/paper/2509.08126/) (2025)
- [LACY: A Vision-Language Model-based Language-Action Cycle for Self-Improving Robotic Manipulation](https://mlsanity.com/paper/2511.02239/) (2025)
- [GraspLDP: Towards Generalizable Grasping Policy via Latent Diffusion](https://mlsanity.com/paper/2602.22862/) (2026)
- [Learning Object-Centric Spatial Reasoning for Sequential Manipulation in Cluttered Environments](https://mlsanity.com/paper/2603.02511/) (2026)
- [SpatialPoint: Spatial-aware Point Prediction for Embodied Localization](https://mlsanity.com/paper/2603.26690/) (2026)

## Abstract (arXiv)

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.