Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2001.05443v1
• Date: Wed, 15 Jan 2020 17:23:55 GMT
• Title: Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning
• Authors: Priya Shukla, Hitesh Kumar and G. C. Nandi
• Abstract summary: Humans almost immediately know how to manipulate objects for grasping due to learning over the years. In this paper we have taken up the challenge of developing learning based pose estimation by decomposing the problem into both position and orientation learning. Based on our proposed architectures and algorithms, the robot is capable of grasping all rigid body objects having regular shapes.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Intelligent Object manipulation for grasping is a challenging problem for robots. Unlike robots, humans almost immediately know how to manipulate objects for grasping due to learning over the years. A grown woman can grasp objects more skilfully than a child because of learning skills developed over years, the absence of which in the present day robotic grasping compels it to perform well below the human object grasping benchmarks. In this paper we have taken up the challenge of developing learning based pose estimation by decomposing the problem into both position and orientation learning. More specifically, for grasp position estimation, we explore three different methods - a Genetic Algorithm (GA) based optimization method to minimize error between calculated image points and predicted end-effector (EE) position, a regression based method (RM) where collected data points of robot EE and image points have been regressed with a linear model, a PseudoInverse (PI) model which has been formulated in the form of a mapping matrix with robot EE position and image points for several observations. Further for grasp orientation learning, we develop a deep reinforcement learning (DRL) model which we name as Grasp Deep Q-Network (GDQN) and benchmarked our results with Modified VGG16 (MVGG16). Rigorous experimentations show that due to inherent capability of producing very high-quality solutions for optimization problems and search problems, GA based predictor performs much better than the other two models for position estimation. For orientation learning results indicate that off policy learning through GDQN outperforms MVGG16, since GDQN architecture is specially made suitable for the reinforcement learning. Based on our proposed architectures and algorithms, the robot is capable of grasping all rigid body objects having regular shapes.

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