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Deep Reinforcement Learning (DRL) is emerging as a promising approach togenerate adaptive behaviors for robotic platforms. However, a major drawback ofusing DRL is the data-hungry training regime that requires millions of trialand error attempts, which is impractical when running experiments on roboticsystems. Learning from Demonstrations (LfD) has been introduced to solve thisissue by cloning the behavior of expert demonstrations. However, LfD requires alarge number of demonstrations that are difficult to be acquired sincededicated complex setups are required. To overcome these limitations, wepropose a multi-subtask reinforcement learning methodology where complex pickand place tasks can be decomposed into low-level subtasks. These subtasks areparametrized as expert networks and learned via DRL methods. Trained subtasksare then combined by a high-level choreographer to accomplish the intended pickand place task considering different initial configurations. As a testbed, weuse a pick and place robotic simulator to demonstrate our methodology and showthat our method outperforms a benchmark methodology based on LfD in terms ofsample-efficiency. We transfer the learned policy to the real robotic systemand demonstrate robust grasping using various geometric-shaped objects.

Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtasks