Motivation in Embodied Intelligence

Before artificial intelligence set its mind on developing abstract intelligent agents which can think, Alan Turing suggested training embodied machines equipped with sensors and actuators to accomplish intelligent tasks like understanding spoken English (Turing, 1950). Looking at intelligence from a different perspective, philosopher, and neuroscientist Francisco Varela (Maturana & Varela, 1980), (Varela et al., 1992) proposed the embodied philosophy of living systems which argues that human cognition can only be understood in terms of the human body and the physical environment with which it interacts. What may seem to be a revelation from a historical perspective, early robots built on cybernetic principles demonstrated goal-seeking behavior, homeostasis (the ability to keep parameters within prescribed ranges), and learning abilities (Walter, 1951), (Walter, 1953). These were precursors for embodied intelligence. Perhaps the most influential figure in developing embodied intelligence as a methodology to design intelligent machines is Rodney Brooks. He suggested the design of intelligent machines through interaction with the environment driven by perception and action, rather than by a prespecified algorithm (Brooks, 1991a). Like Hans Moravec before him (Moravec, 1984), Brooks suggested that locomotion and vision are fundamental for natural intelligence. He also observed that the environment is its best model and that representation is the wrong “unit of abstraction”. These simple observations revolutionized the way people think about intelligent machines and created a field of research called “embodied intelligence”. The growth of interest in embodied intelligence that followed Brooks’ works can be compared to the increase in research activities in artificial intelligence that followed the famous Dartmouth Conference of 1956 (McCarthy et al., 1955) or the revival of neural network research in the 1980s. His approach revived the field of autonomous robots, but as robotics thrived, research on embodied intelligence started to concentrate on the commercial aspects of robots with a lot of effort spent on embodiment and a little on intelligence. The open question remains: how to continue on the path to machine intelligence? Today, once again, artificial intelligence research is focused on specialized problems, such as ways to represent knowledge, natural language and scene understanding, semantic cognition, question answering, associative memories or various forms or reinforcement learning. In recent years, the term “general artificial intelligence” was coined as something new, incorrectly implying that the original idea of AI was something less than to develop a natural intelligence.