Adaptable Automation with Modular Deep Reinforcement Learning and Policy Transfer

The need for "intelligence" in such automation systems stems from the fact that most robotic operations in industry are currently limited to rote and repetitive tasks performed within structured environments. This leaves an entire swath of more complex tasks with high degrees of uncertainty and dynamic environments [7] difficult or even impossible to automate. Examples include maintenance and material handling for producing the desired product in manufacturing systems [8], robot surgeries and pharmacy automation in healthcare systems [9], safe working environments in disaster management for deep-sea operation, and nuclear energy [10], fruit picking, crop sensing, and selective weeding in agriculture systems [11]. A fundamental question concerning the notion of intelligent automation in this context then becomes: How can we enable adaptable industrial automation systems that can analyze and act upon their perceived environment rather than merely executing a set of predefined programs? Adaptability is among the key characteristics of industrial automation systems in response to unpredictable changes or disruptions in the process [12].