Learning Manipulation Tasks in Dynamic and Shared 3D Spaces

Abstract--Automating the segregation process is a need for every sector experiencing a high volume of materials handling, repetitive and exhaustive operations, in addition to risky exposures. Learning automated pick-and-place operations can be efficiently done by introducing collaborative autonomous systems (e.g. In this paper, we propose a deep reinforcement learning strategy to learn the place task of multi-categorical items from a shared workspace between dual-manipulators and to multi-goal destinations, assuming the pick has been already completed. The learning strategy leverages first a stochastic actor-critic framework to train an agent's policy network, and second, a dynamic 3D Gym environment where both static and dynamic obstacles (e.g. When one robot fails and undergoes maintenance, the left-overs items from warehouses and manufacturing industries operation will not stop but will be pursued by the other (e.g bolts and nuts), multi-categorical waste (e-waste, sanitary agent in the same workspace.