  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.
