Real-Time Deep Learning Approach to Visual Servo Control and Grasp Detection for Autonomous Robotic Manipulation

TL;DR

This paper presents a real-time deep learning system for visual perception, grasp detection, and visual servo control in autonomous robotic manipulation, achieving high accuracy and generalization in dynamic environments.

Contribution

The work introduces a novel real-time neural network framework for combined grasp detection and visual servoing, trained on automatically generated datasets for improved accuracy and speed.

Findings

Achieved millimeter-level accuracy in grasp positioning.

Demonstrated real-time performance in dynamic environments.

Validated system effectiveness on a Kinova Gen3 robot.

Abstract

In order to explore robotic grasping in unstructured and dynamic environments, this work addresses the visual perception phase involved in the task. This phase involves the processing of visual data to obtain the location of the object to be grasped, its pose and the points at which the robot`s grippers must make contact to ensure a stable grasp. For this, the Cornell Grasping dataset is used to train a convolutional neural network that, having an image of the robot`s workspace, with a certain object, is able to predict a grasp rectangle that symbolizes the position, orientation and opening of the robot`s grippers before its closing. In addition to this network, which runs in real-time, another one is designed to deal with situations in which the object moves in the environment. Therefore, the second network is trained to perform a visual servo control, ensuring that the object remains in the robot`s field of view. This network predicts the proportional values of the linear and angular velocities that the camera must have so that the object is always in the image processed by the grasp network. The dataset used for training was automatically generated by a Kinova Gen3 manipulator. The robot is also used to evaluate the applicability in real-time and obtain practical results from the designed algorithms. Moreover, the offline results obtained through validation sets are also analyzed and discussed regarding their efficiency and processing speed. The developed controller was able to achieve a millimeter accuracy in the final position considering a target object seen for the first time. To the best of our knowledge, we have not found in the literature other works that achieve such precision with a controller learned from scratch. Thus, this work presents a new system for autonomous robotic manipulation with high processing speed and the ability to generalize to several different objects.