# 2.5D Image based Robotic Grasping

## TL;DR

This paper presents a real-time neural network approach for robotic grasping that fuses depth and RGB data, demonstrating competitive performance in physical robot experiments with robustness to observation height.

## Contribution

The authors introduce a novel encoder-decoder neural network that combines depth and RGB images for improved robotic grasping in real time.

## Key findings

- Achieves real-time grasp prediction on a physical robot
- Fuses depth and RGB data effectively for robust grasping
- Performs competitively with state-of-the-art methods

## Abstract

We consider the problem of robotic grasping using depth + RGB information sampling from a real sensor. we design an encoder-decoder neural network to predict grasp policy in real time. This method can fuse the advantage of depth image and RGB image at the same time and is robust for grasp and observation height.We evaluate our method in a physical robotic system and propose an open-loop algorithm to realize robotic grasp operation. We analyze the result of experiment from multi-perspective and the result shows that our method is competitive with the state-of-the-art in grasp performance, real-time and model size. The video is available in https://youtu.be/Wxw_r5a8qV0