Introduction to developmental robotics

Abstract

Developmental robotics is a broad, new discipline that lies at the intersections of psychology, biology, artificial intelligence (AI) and robotics. This new field was inspired by the fact that most complex and intelligent biological organisms (as opposed to artificial ones) undergo an extended period of development before reaching their adult form and adult abilities. This new rubric captures the essential features of many related, previous research agendas, including embodied cognition, evolutionary robotics and machine learning. Although developmental robotics combines many of these previous efforts, it also has fundamental differences that separate it in a number of interesting ways. To appreciate these differences, it is useful to reflect on the history of robotics and AI. Since the inception of AI in the 1950s, its practitioners have been striving to create intelligent machines. There have been some notable successes in restricted domains, such as game playing. However, the vision of creating general-purpose, human-like intelligence has not yet been achieved. To date, there have been three primary approaches to trying to create intelligent robots: direct programming; supervised machine learning; and evolutionary adaptation (Weng et al. 2001). In direct programming, a human engineer analyses the problem domain, determines a solution and then implements the solution in a program. Here the intelligence resides solely in the human engineer, the robot is merely acting out the pre-programmed commands. Robots created by direct programming tend to be brittle and fail in new situations not anticipated by the human engineer. In supervised learning, a human engineer creates a series of training situations describing how the robot should respond to particular sensory inputs. The robot learns to mimic the training data and typically makes useful generalizations that apply to novel situations that were never seen during training. This is an improvement over direct programming in that the robot, rather than the human engineer, determines how to solve the problem, and the robot can go beyond what it was initially exposed to, leading to more robust behaviour. However,