  In this work, we delve into the intricate synergy among non-prehensile
actions like pushing, and prehensile actions such as grasping and throwing,
within the domain of robotic manipulation. We introduce an innovative approach
to learning these synergies by leveraging model-free deep reinforcement
learning. The robot's workflow involves detecting the pose of the target object
and the basket at each time step, predicting the optimal push configuration to
isolate the target object, determining the appropriate grasp configuration, and
inferring the necessary parameters for an accurate throw into the basket. This
empowers robots to skillfully reconfigure cluttered scenarios through pushing,
creating space for collision-free grasping actions. Simultaneously, we
integrate throwing behavior, showcasing how this action significantly extends
the robot's operational reach. Ensuring safety, we developed a simulation
environment in Gazebo for robot training, applying the learned policy directly
to our real robot. Notably, this work represents a pioneering effort to learn
the synergy between pushing, grasping, and throwing actions. Extensive
experimentation in both simulated and real-robot scenarios substantiates the
effectiveness of our approach across diverse settings. Our approach achieves a
success rate exceeding 80\% in both simulated and real-world scenarios. A video
showcasing our experiments is available online at: https://youtu.be/q1l4BJVDbRw
