The integration of Deep Reinforcement Learning (DRL) into the realm of robotics and autonomous systems has emerged as a groundbreaking paradigm shift, empowering machines to tackle intricate tasks through interaction with their environments. This chapter offers a comprehensive examination of the current research landscape at the intersection of DRL and robotics within this dynamic field. This chapter navigates through the conceptualization of DRL and explores its diverse applications in controlling robotics and object manipulation. The chapter showcases the autonomy and adaptability enabled by DRL while addressing prevalent challenges such as sample efficiency, safety concerns, and scalability. In conclusion, this chapter serves as a valuable resource for future researchers and practitioners intrigued by the intersection of DRL and robotics. It synthesizes current knowledge, underscores significant progress made, and maps out exciting avenues for further exploration, ultimately propelling the advancement of robotic systems in the era of machine learning and artificial intelligence.
Top1. Introduction
The integration of DRL with robotics stands as a revolutionary collaboration driving modern technological progress. As posited by previous studies, Reinforcement learning serves as a computational framework for comprehending and automating the acquisition of intelligent behaviors in autonomous entities (Sutton and Barto, 2018). It is defined as a subset of machine learning that underscores the cultivation of agents capable of learning optimal behaviors through interactions with their environments. Within the domain of robotics, this approach has materialized into a compelling convergence poised to redefine the scope of machine capabilities. This mirrors the iterative process of human learning, wherein actions are reinforced by rewards or penalties, facilitating the discernment of advantageous strategies. Traditionally, in robotics, this involved meticulous rule-based programming to govern behavior. However, the advent of DRL marks a significant departure from this conventional approach.
Robots equipped with DRL algorithms have displayed striking competencies, such as automated navigation in volatile circumstances and dynamic product management (Lei et al., 2020). In contemporary times, robots have transcended the reliance on predetermined protocols and have acquired the capacity to acquire knowledge from historical encounters, adjust to unanticipated circumstances, and enhance their strategies to enable them to effectively address unforeseen barriers (Licardo et al., 2024; Dwivedi et al., 2022). This radical transformation exhibits significant potential across various domains, including the optimization of industrial operations, the strengthening of medical care, and the emergence of autonomous automobiles (Nath et al., 2020).
While the potential of DRL in the field of robotics is readily apparent, it is accompanied by notable challenges that warrant attention. (Virani et al., 2021; Byun & Nam, 2022). Concerns such as scalability, sample efficiency, and safety remain crucial factors in the implementation of Deep Reinforcement Learning (DRL) in practical settings (Parvez Farazi et al., 2021; Liu et al., 2022; Hessel et al., 2018). As a result, this discipline remains constantly growing, developing various programs, defining standardized circumstances, and upgrading the parameters for assessment. Such programs strive to efficiently address the aforementioned adversities and encourage the overall potential of DRL in the domain of robotics. Within the domain of robotics, Deep Reinforcement Learning (DRL) ushers in a novel era characterized by heightened adaptability and autonomy. DRL introduces the potency of neural networks and deep learning into the realm of reinforcement learning. The substantial progression in computational capacity grants autonomy to robotic systems and enables them to traverse environments that are extraordinarily complex and ever-changing (Tsitsimpelis et al., 2019; Cugurullo, 2020). The core of DRL abides by its capability to efficiently process intricate sensory inputs, deduce optimal actions via iterative refinement, and interpret abstract representations. The aforementioned self-improvement mechanism has been instrumental in the education of robotics, facilitating their adept performance across a wide spectrum of activities, including independent navigation, obstacle avoidance, and accurate object manipulation.
This chapter undertakes an in-depth investigation into the evolving landscape of DRL within the realms of robots. Through meticulous scrutiny of existing studies, the current study seeks to clarify crucial advancements, approaches, applications, and barriers that are defining this dynamic intersection. By shedding light on the transformative trajectory of DRL within these fields, the chapter endeavors to offer academicians, professional experts, and individuals a holistic viewpoint, empowering them to harness DRL's transformative capabilities in shaping the future of robotic innovations.