  With the advent of artificial intelligence and machine learning, humanoid
robots are made to learn a variety of skills which humans possess. One of
fundamental skills which humans use in day-to-day activities is performing
tasks with coordination between both the hands. In case of humanoids, learning
such skills require optimal motion planning which includes avoiding collisions
with the surroundings. In this paper, we propose a framework to learn
coordinated tasks in cluttered environments based on DiGrad - A multi-task
reinforcement learning algorithm for continuous action-spaces. Further, we
propose an algorithm to smooth the joint space trajectories obtained by the
proposed framework in order to reduce the noise instilled during training. The
proposed framework was tested on a 27 degrees of freedom (DoF) humanoid with
articulated torso for performing coordinated object-reaching task with both the
hands in four different environments with varying levels of difficulty. It is
observed that the humanoid is able to plan collision free trajectory in
real-time. Simulation results also reveal the usefulness of the articulated
torso for performing tasks which require coordination between both the arms.
