Vision-Based Robotic Arm Control Algorithm Using Deep Reinforcement Learning for Autonomous Objects Grasping
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TL;DR: A deep deterministic policy gradient approach that can be applied to a numerous-degrees-of-freedom robotic arm towards autonomous objects grasping according to their classification and a given task and its simulation results reveal its advantages compared to the traditional one.
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Abstract: While working side-by-side, humans and robots complete each other nowadays, and we may say that they work hand in hand. This study aims to evolve the grasping task by reaching the intended object based on deep reinforcement learning. Thereby, in this paper, we propose a deep deterministic policy gradient approach that can be applied to a numerous-degrees-of-freedom robotic arm towards autonomous objects grasping according to their classification and a given task. In this study, this approach is realized by a five-degrees-of-freedom robotic arm that reaches the targeted object using the inverse kinematics method. You Only Look Once v5 is employed for object detection, and backward projection is used to detect the three-dimensional position of the target. After computing the angles of the joints at the detected position by inverse kinematics, the robot’s arm is moved towards the target object’s emplacement thanks to the algorithm. Our approach provides a neural inverse kinematics solution that increases overall performance, and its simulation results reveal its advantages compared to the traditional one. The robot’s end grip joint can reach the targeted location by calculating the angle of every joint with an acceptable range of error. However, the accuracy of the angle and the posture are satisfied. Experiments reveal the performance of our proposal compared to the state-of-the-art approaches in vision-based grasp tasks. This is a new approach to grasp an object by referring to inverse kinematics. This method is not only easier than the standard one but is also more meaningful for multi-degrees of freedom robots.
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Citations
An enhanced deep deterministic policy gradient algorithm for intelligent control of robotic arms
TL;DR: Aiming at the poor robustness and adaptability of traditional control methods for different situations, the deep deterministic policy gradient (DDPG) algorithm is improved by designing a hybrid function that includes different rewards superimposed on each other as discussed by the authors .
An Object Recognition Grasping Approach Using Proximal Policy Optimization With YOLOv5
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References
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•Proceedings Article
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov,Alex Irpan,Peter Pastor,Julian Ibarz,Alexander Herzog,Eric Jang,Deirdre Quillen,Ethan Holly,Mrinal Kalakrishnan,Vincent Vanhoucke,Sergey Levine +10 more
- 27 Jun 2018
TL;DR: QT-Opt as mentioned in this paper is a scalable self-supervised vision-based reinforcement learning framework that can leverage over 580k real-world grasp attempts to train a deep neural network Q-function with over 1.2M parameters.
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Learning Synergies Between Pushing and Grasping with Self-Supervised Deep Reinforcement Learning
Andy Zeng,Shuran Song,Stefan Welker,Johnny Lee,Alberto Rodriguez,Thomas Funkhouser +5 more
- 27 Mar 2018
TL;DR: This work demonstrates that it is possible to discover and learn complex synergies between non-prehensile and prehensile actions from scratch through model-free deep reinforcement learning, and achieves better grasping success rates and picking efficiencies than baseline alternatives after a few hours of training.