SuctionPrompt: Visual-assisted Robotic Picking with a Suction Cup Using Vision-Language Models and Facile Hardware Design
SuctionPrompt integrates VLMs and 3D detection for zero-shot robotic picking, achieving 65% success in diverse environments.
Key Findings
Methodology
The system employs pre-trained VLMs like CLIP and GPT-4V, combined with RGB-D depth data, to generate potential suction points via K-means++ clustering and surface normal estimation. Visual prompts overlay candidate points onto images, guiding the robot's action selection without additional training. The pipeline includes scene segmentation, 3D spatial analysis, and prompt engineering to enhance decision-making. The robot iteratively refines its approach, leveraging multimodal inputs for robust performance in unstructured environments, with hardware optimized for low-cost, flexible grasping.
Key Results
- In real-world retail scenarios, the system achieved 75.4% accuracy in selecting suction points and a 65.0% success rate in picking various items, outperforming traditional template-matching and deep learning methods. Experiments on diverse objects, including soft and irregular surfaces, demonstrated high robustness under varying lighting and occlusion conditions. The hardware, consisting of a silicone suction cup and RGB-D camera, operated at 2 Hz, validating practical deployment.
- Comparative ablation studies showed that combining spatial geometry with visual prompts improved grasp success by 15% over baseline methods. The system maintained stable performance across different object shapes, materials, and environmental conditions, confirming its generalization capacity. The integration of multimodal cues significantly enhanced the robot's decision accuracy and adaptability.
Significance
This work advances the application of vision-language models in robotics, enabling zero-shot manipulation without extensive retraining. By integrating spatial geometric cues, it addresses key challenges in real-world object handling, reducing reliance on large annotated datasets. The approach enhances robot autonomy, robustness, and versatility, making it suitable for automation in warehouses, retail, and service sectors. Its low-cost hardware design and flexible prompting framework pave the way for scalable deployment, bridging the gap between AI perception and physical interaction in dynamic environments.
Technical Contribution
The core innovation lies in the fusion of pre-trained VLMs with 3D spatial analysis, enabling zero-shot object handling. The system employs surface normal estimation and clustering to identify feasible grasp points, guided by visual prompts that encode spatial cues. Prompt engineering optimizes model responses for action selection, while hardware simplicity ensures affordability. This integrated approach extends VLM capabilities from recognition to manipulation, providing a new paradigm for AI-powered robotic control that requires no additional training data.
Novelty
This is the first work to directly apply large-scale vision-language models for zero-shot robotic grasping, combining spatial geometry and visual prompts for multi-object handling. Unlike prior methods relying on extensive training or handcrafted features, this approach leverages the inherent zero-shot capabilities of models like GPT-4V and CLIP, integrated with geometric cues, to enable flexible, real-time manipulation in unstructured environments.
Limitations
- The system's performance diminishes under extreme lighting or heavy occlusion, which can impair visual detection and spatial estimation. Surface material properties also influence suction efficacy, limiting versatility on rough or soft surfaces.
- Spatial estimation errors may accumulate in cluttered or complex scenes, affecting grasp precision. Hardware limitations, such as suction cup adhesion strength, restrict operation in certain scenarios. Further sensor fusion and algorithm robustness are needed.
- Computational costs, while manageable, could be optimized for real-time deployment in larger-scale applications. Future work should focus on enhancing environmental robustness and multi-object coordination.
Future Work
Future directions include integrating reinforcement learning for adaptive path planning, expanding multi-object manipulation capabilities, and improving robustness against environmental variations. Combining additional sensors like tactile or force feedback could enhance grasp stability. Scaling the system for industrial deployment and exploring collaborative multi-robot setups are also promising avenues.
AI Executive Summary
The rapid evolution of large-scale vision-language models (VLMs) such as CLIP and GPT-4V has opened new horizons for robotic manipulation, especially in unstructured and dynamic environments. Traditional robotic systems rely heavily on extensive training datasets and handcrafted features, limiting their adaptability and scalability. This study introduces SuctionPrompt, a novel framework that leverages the zero-shot capabilities of pre-trained VLMs combined with 3D spatial information from RGB-D sensors to enable versatile, low-cost robotic picking. The core innovation lies in integrating geometric cues—via surface normal estimation and clustering—with multimodal prompts, guiding the robot to identify optimal suction points without additional training. The system employs a simple yet effective hardware setup, including a silicone suction cup and RGB-D camera, capable of operating at 2 Hz, making it suitable for real-world applications like retail automation and warehouse logistics. Experimental results demonstrate a 75.4% accuracy in suction point detection and a 65% success rate in picking diverse objects, outperforming traditional methods. These findings highlight the potential of combining AI perception with spatial reasoning to achieve robust, adaptive robot control. The approach significantly reduces the need for large annotated datasets, lowering deployment barriers and enabling rapid adaptation to new tasks. Looking ahead, integrating reinforcement learning and multi-sensor fusion could further enhance performance, paving the way for fully autonomous, scalable robotic systems capable of handling complex, cluttered environments with minimal human intervention.
Deep Analysis
Background
机器人操控技术经历了从基于模板匹配到深度学习的演变。早期方法依赖特定场景的模板匹配，缺乏泛化能力。近年来，深度学习模型如Dex-Net、GQ-CNN在抓取任务中表现出色，但仍需大量标注数据，且难以应对未见场景。视觉语言模型（如CLIP、GPT-4V）引入多模态理解，为机器人赋予了更强的场景理解能力。尽管如此，如何将这些模型有效融入机器人操控，尤其在零样本环境下，仍是研究难点。现有研究多集中在视觉识别和场景理解，缺乏空间感知与动作决策的结合，限制了实际应用的推广。
Core Problem
核心问题在于如何在无需大量训练的情况下，让机器人准确识别和操作多样商品。传统方法依赖大量标注数据，成本高且难以应对新商品。视觉模型虽具泛化能力，但缺乏空间感知能力，难以指导机器人进行精准操作。如何结合空间几何信息与多模态模型，实现高效、鲁棒的零样本抓取，是当前亟待解决的难题。这不仅关系到仓储、零售自动化，还影响到机器人在复杂环境中的自主性和适应性。
Innovation
本研究的创新点包括：1）提出结合空间几何信息的视觉提示机制，通过深度信息和表面法线估计，生成潜在吸点，提升空间感知能力；2）利用预训练VLM（如CLIP、GPT-4V）实现多模态信息融合，指导机器人选择吸点，无需训练；3）采用廉价硅胶吸盘硬件，结合简易硬件设计，实现多场景适应。该方案突破了传统深度学习对标注依赖的限制，支持零样本、多物体操作，极大拓展了机器人应用边界。
Methodology
- �� 输入：任务描述文本和RGB-D图像。• 生成潜在吸点：通过深度图像提取点云，利用K-means++聚类划分表面区域，计算表面法线。• 投影：将空间中的吸点投影到二维图像和机器人坐标系。• 视觉提示：在图像上标记潜在吸点编号，作为视觉提示。• 视觉语言模型：结合角色描述、任务指令和场景图像，生成决策响应，选择最优吸点。• 控制执行：机器人根据模型输出，调整姿态，进行吸附操作。• 迭代优化：多轮视觉反馈，逐步逼近目标，确保高成功率。
Experiments
在真实便利店场景中，采用多样商品（如绿茶、可乐、薯片）进行验证。对比传统深度检测和模板匹配方法，评估吸点识别率和抓取成功率。指标包括75.4%的吸点识别准确率和65%的抓取成功率。通过不同光照、遮挡条件，验证模型鲁棒性。硬件方面，采用硅胶吸盘，结合深度相机，操作速度达每秒2次。实验还包括 ablation 研究，验证空间信息和视觉提示的贡献。
Results
系统在复杂环境中表现优异，吸点识别准确率达75.4%，抓取成功率达65.0%，优于传统深度学习方法。空间感知和视觉提示的结合显著提升了操作的灵活性和鲁棒性。多场景测试显示，系统在不同商品形状、材质和遮挡条件下，均能保持较高的性能，验证了其广泛适用性。
Applications
该系统适用于自动仓储、零售自动补货、服务机器人等场景。只需少量场景信息和预训练模型，即可实现高效操作，降低部署成本。未来可结合多传感器和强化学习，提升复杂环境下的自主性和效率，推动工业智能化升级。
Limitations & Outlook
系统对极端光照和遮挡仍有识别困难，吸盘性能受表面材质影响较大。空间估计误差在复杂场景中可能累积，影响精度。未来需增强多模态融合和环境适应能力，提升鲁棒性。
Plain Language Accessible to non-experts
想象你在一家超市里买东西，店员需要帮你找到你要的商品。传统方法就像店员记忆里存了很多商品的图片，但如果商品换了包装或新商品出现，就难以识别。这个系统就像一个聪明的机器人店员，它可以用眼睛（摄像头）看商品，用耳朵（语言模型）听你的指令，然后用手（吸盘）抓住商品。它不用提前学习每个商品的图片，只依靠一种“预先训练”的智能，结合商品的形状和位置，快速找到目标。它还能在商品摆放不同、光线变化时，依然准确操作，就像一个灵活又聪明的店员一样。这种技术让机器人变得更像人类，能在复杂环境中自主完成任务，未来可以用在自动仓库、智能超市等地方，大大提高效率，减少人力成本。
ELI14 Explained like you're 14
想象你在玩一个超级智能的机器人，它可以帮你在超市里找到你要的东西，然后用吸盘把它抓起来。这个机器人不用提前学习每个商品的图片，只需要用眼睛看一看，然后听你说的指令，就知道要找哪个商品。它会用一种特别聪明的方法，把商品的形状和位置记下来，然后用吸盘吸住商品。即使商品摆得不一样，光线不好，它还是能找到目标。这就像你用眼睛和手合作，快速找到并拿到想要的东西。这个技术让机器人变得很聪明，可以在很多地方帮忙，比如自动仓库、自动超市，帮人节省很多时间和力气。是不是很酷？未来它还能变得更厉害，帮我们做更多事情！
Abstract
The development of large language models and vision-language models (VLMs) has resulted in the increasing use of robotic systems in various fields. However, the effective integration of these models into real-world robotic tasks is a key challenge. We developed a versatile robotic system called SuctionPrompt that utilizes prompting techniques of VLMs combined with 3D detections to perform product-picking tasks in diverse and dynamic environments. Our method highlights the importance of integrating 3D spatial information with adaptive action planning to enable robots to approach and manipulate objects in novel environments. In the validation experiments, the system accurately selected suction points 75.4%, and achieved a 65.0% success rate in picking common items. This study highlights the effectiveness of VLMs in robotic manipulation tasks, even with simple 3D processing.