# Improving Data Efficiency of Self-supervised Learning for Robotic Grasping

## TL;DR

This paper presents a data-efficient learning algorithm for robotic grasping using depth images and force feedback, achieving high success rates with significantly less training data than previous methods.

## Contribution

The authors introduce a geometric consistency-based approach and systematic exploration of grasp space, reducing training data requirements by an order of magnitude for robotic grasping.

## Key findings

- Achieved 96.6% grasp success rate in bin picking scenarios.
- Learned with only 23,000 grasp attempts over 60 hours.
- System generalizes well to novel objects and environments.

## Abstract

Given the task of learning robotic grasping solely based on a depth camera input and gripper force feedback, we derive a learning algorithm from an applied point of view to significantly reduce the amount of required training data. Major improvements in time and data efficiency are achieved by: Firstly, we exploit the geometric consistency between the undistorted depth images and the task space. Using a relative small, fully-convolutional neural network, we predict grasp and gripper parameters with great advantages in training as well as inference performance. Secondly, motivated by the small random grasp success rate of around 3%, the grasp space was explored in a systematic manner. The final system was learned with 23000 grasp attempts in around 60h, improving current solutions by an order of magnitude. For typical bin picking scenarios, we measured a grasp success rate of 96.6%. Further experiments showed that the system is able to generalize and transfer knowledge to novel objects and environments.