Grasp detection: Datasets & Models
Grasp detection identifies where and how a robotic gripper can pick up an object in a scene, locating graspable regions and objects for pick-and-place tasks.
Grasp detection
This detection workflow analyzes images from a robot camera or overhead view and locates objects along with the regions where a gripper can pick them up. Trained on a grasp dataset, it returns bounding boxes for graspable objects on work surfaces, bins, conveyors, or cluttered tables to support pick-and-place tasks.
Try it out
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Why automate grasp detection
Manually programming pick points for every part variant is slow and breaks the moment an object shifts, rotates, or arrives in an unstructured pile. A grasp detection model lets a robot adapt to novel object positions and mixed SKUs without rewriting logic for each case, which is essential for bin picking, machine tending, and order fulfillment in robotics and automation cells. Instead of rigid fixturing and hand-tuned coordinates, you get vision-driven picking that tolerates clutter and variation.
Key benefits
Teams can train on their own grasp dataset and deploy to existing arm-mounted or overhead cameras, cutting integration effort versus custom sensor rigs. Detection-based grasping scales across product lines and lines up cleanly with motion planners, and using RF-DETR gives strong accuracy on cluttered, overlapping objects where reliable pick points matter most.
How to use it
- Upload an image: Drop any photo into the widget above to try grasp detection for free, right on this page.
- Preview instant results: The model detects cardboard box, metal bracket, plastic bottle, screw, fabric bag within seconds and returns an annotated image with structured predictions, so you can immediately see how well it works. Adjust the detection classes and re-run until the results fit your scenario.
- Like what you see?: With a free Roboflow account, you can deploy this workflow with the Serverless Cloud API, customize it in your workspace, or train RF-DETR on your own images for production accuracy.
- Get started for free: No code or training data required to begin: upload, review the results, and make it part of your stack in a single click.
Recommended model for grasp detection
RF-DETR is state of the art for real-time object detection, instance segmentation, and keypoint detection, with higher accuracy and lower latency than comparable models, and it's Apache 2.0 licensed. When you're ready to build a production model for grasp detection, fine-tune it on your own labeled images, then deploy to a Serverless Cloud API endpoint.
Not sure where to start?
Describe your Grasp Detection project to Roboflow Agent.