Curriculum-Based Deep Reinforcement Learning for Adaptive Robotics: A Mini-Review

Abstract

To facilitate the current and future automation needs, the research community constantly seeks to develop dynamic and efficient autonomous decision-making agents. These agents must not only be robust to modeling uncertainties, internal and external changes, but can adapt to a range of tasks also. Recent progress in deep reinforcement learning has corroborated to its potential to train such autonomous and robust agents. At the same time, the introduction of curriculum learning has made the reinforcement learning process significantly more efficient and allowed for training on much broader tasks. This combination, Curriculum-based Deep Reinforcement Learning (CDRL), presents a powerful solution to meet the increasing complexity of today's automation industry that demands highly intelligent machines. With this work we present a concise review of CDRL methods within the context of their application to the field of adaptive robotics.

The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works

“…However, developing robots outside traditional automation presents incomparable obstacles, particularly for real-world applications, such as autonomous decision making and cognitive awareness. Modern adaptable robots have a lot of promise, and the integration of AI and machine learning has sparked the attention of many different study fields [24]. End-to-end autonomy in learning-based robots commonly includes three primary elements, such as perception, cognition, and control.…”

“…As a segment of AI, machine learning refers to algorithmic or statistical operations that allow computer systems to learn from experience automatically [24]. An interconnected industry with a network of industrial Internet-of-Things (IIoT) devices, such as robotics that improve and optimize processes as part of the smart manufacturing process, is made possible largely through machine learning.…”