Real-Time, Highly Accurate Robotic Grasp Detection using Fully Convolutional Neural Networks with High-Resolution Images

Understand

Robotic grasp detection for novel objects is a challenging task, but for the last few years, deep learning based approaches have achieved remarkable performance improvements, up to 96.1% accuracy, with RGB-D data.

• In this paper, we propose fully convolutional neural network (FCNN) based methods for robotic grasp detection.
• Our methods also achieved state-of-the-art detection accuracy (up to 96.6%) with state-of- the-art real-time computation time for high-resolution images (6-20ms per 360x360 image) on Cornell dataset.
• Due to FCNN, our proposed method can be applied to images with any size for detecting multigrasps on multiobjects.