32 papers with code • 3 benchmarks • 10 datasets
End-to-end control for robot manipulation and grasping is emerging as an attractive alternative to traditional pipelined approaches.
The IKEA Furniture Assembly Environment is one of the first benchmarks for testing and accelerating the automation of complex manipulation tasks.
Since product images are readily available for a wide range of objects (e. g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data.
In this paper, we present a modular robotic system to tackle the problem of generating and performing antipodal robotic grasps for unknown objects from n-channel image of the scene.
Ranked #1 on Robotic Grasping on Jacquard dataset
Enter the RobotriX, an extremely photorealistic indoor dataset designed to enable the application of deep learning techniques to a wide variety of robotic vision problems.
We show that a mild relaxation of the task and workspace constraints implicit in existing object grasping datasets can cause neural network based grasping algorithms to fail on even a simple block stacking task when executed under more realistic circumstances.
Our novel grasp representation treats 3D points of the recorded point cloud as potential grasp contacts.