This research aims to derive and apply a principled and practical framework to study developmental autonomous behavior (DAB) of machines. DAB refers to “the general ability of a machine to acquire new skills and behavior from its birth to maturity on its own without human intervention” [1]. It draws inspiration from ethology, the study of animal behavior, to derive the ultimate-proximate explanations of the causality, mechanisms, and circumstances of emergent behavior that are grounded in the physical interactions and sensory experiences of machines in their environment. The key difference of this research from other related fields, such as cognitive science, artificial intelligence, and developmental robotics, is its objective and perspective. Unlike others, this research does not aim to understand human cognition or build a machine that replicates human capabilities. Instead, it strictly focuses on identifying the systems and processes that achieve autonomy from the perspective of machines. Because of this objective and perspective, this research is free from the obligation to justify theories with respect to intangible, ill-defined qualities of humans, such as beliefs, consciousness, curiosity, intelligence, motivation, sentience, and thinking. By placing these human qualities outside the scope, this research is free to close the loop between deductive and inductive analyses of machine behavior with direct access to the machines’ internals, which is a major obstacle in human studies. This research deepens our understanding of autonomous machines, which is paramount to the safety of our society and thus a critical area of exploration for the advancement of automation science and engineering.
The concept of autonomy is central in this research. Based on the principles of embodiment and situatedness, the proposed framework defines autonomy in the ecological sense of survival and operation continuity. Machine autonomy is therefore tested in terms of an ability to resupply its own energy without human intervention. The framework posits that value systems for the purpose of autonomy drive successive development of memory functions, resulting in progressive changes in behavior from innate reflexive to episodic, procedural, and autonomic behavior. The framework also derives a logical explanation for the transformation process that enables a physical sensorimotor system to become a symbol-like concept processor, fostering conceptual and social behavior development. By precisely defining the concept of autonomy and the role of value systems, the framework provides the principles of developmental autonomous behavior emerging from experience and learning.
The framework is structured for utility to build and analyze developmental autonomous machines by incorporating two practical tools: machine learning (ML) and cyber-physical systems (CPS). It provides a unified understanding of how different ML algorithms contribute to emergent behavior, considering their specific purposes and functions within an integrated system. It also provides a general architecture that leverages distributed computing and communication capabilities of CPS, necessary for continuous autonomous operation in practical applications. For references, the fundamental principles are described in [1]. The concept is demonstrated in a minimally configured mobile robot in [2]. The taxonomy of autonomy and emergent behavior, as well as the role of ML algorithms are described in [3]. The general architecture that leverages CPS and ML is described in [4]. Current works are underway on the topics of value system analysis, morphology, and the playbook (over-the-air operation instruction generation and transfer systems and processes).
[1] S. Isaka, “Developmental Autonomous Behavior: An Ethological Perspective to Understanding Machines,” IEEE Access, vol. 11, pp. 17375-17423, 2023.
[2] S. Isaka, “An Ethological Analysis of Developmental Behavior in Machines," 2023 IEEE International Conference on Development and Learning (ICDL), Macau, China, pp. 79-86, 2023.
[3] S. Isaka, “Autonomy in Cognitive Development of Robots: Embracing Emergent and Predefined Knowledge and Behavior,” Proc. IEEE 20th International Conference on Automation Science and Engineering (CASE), Bari, Italy, 2024, Submitted for publication.
[4] S. Isaka, “Behavior-Based Robotics, Cyber-Physical Systems, and Machine Learning: United as a Practical Pathway Toward Machine Autonomy,” Proc. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Kuching, Malaysia, 2024, Submitted for publication.