# Gaussian based

• lightweight generative architecture
• a grasping representation based on Gaussian kernel
• a Receptive Field Block is assembled to the bottleneck of grasping detection architecture
• Combined pixel attention and channel attention

Grasping is a challenge task for robots: perception, planning and extection.

• We propose a Gaussian-based grasping representation, which relects the maximum grasping score at the center point location and can signigicantly improve the grasping detection accuracy.
• We develope a lightweight generative architecture which achieves high detection accuracy and real-time running speed with small network parameters.
• A receptive field block module is embedded in the bottleneck of the network to enhance its feature discriminability and robustness, and a multi-dimensional attention fusion network is developed to suppress redundant features and enhance target features in the fusion process.
• Evaluation on the public Cornell and Jacquard grasping datasets demonstrate that the proposed generative based grasping detection algorithm achieves state-of-the-art performance of both speed and detection accuracy

### Oriented rectangle-based representation¶

• Analytic methods: use mathematical and physical models in geometry, motion and dynamics to carry out the calculation for grasping
• Empirical methods: deep learning
• Classification-based: Proposals, GQ-CNN, Spatial Transformer Network
• Regression-based: Multi-model fusion, ROI => more inclined to learn the mean value of the ground truth grasps
• Vision and tactic sensing are fuse

### Point-based Grasp representation¶

GGCNN

Orientation Attentive Grasping Detection

## Gaussian-based¶

$G_{K}=\left\{\Phi, W, Q_{K}\right\} \in \mathbb{R}^{3 \times W \times H}$

where,

$Q_{K}=K(x, y)=\exp \left(-\frac{\left(x-x_{0}\right)^{2}}{2 \sigma_{x}^{2}}-\frac{\left(y-y_{0}\right)^{2}}{2 \sigma_{y}^{2}}\right)$

where,

$\sigma_{x}=T_{x}, \sigma_{y}=T_{y}$

### Questions¶

What about classification? In some of the implementations they also output class probabilities.