Introduction to developmental robotics

Künstliche Intell., 2021

2007

Abstract Developmental Robotics provides a promising avenue for uncovering, exploring, testing, and validating hypothesis about the computational nature of intelligence. In this document, I define developmental robotics, and make a case for it as a promising avenue for research. I then discuss two areas of intelligent action that are amenable developmental robotics techniques: integrating information across modalities, and gathering the specific information that will help an developing robot achieve goals.

Artificial intelligence, 1994

2005

2002

It has been proved to be extremely challenging for humans to program a robot to such a sufficient degree that it acts properly in a typical unknown human environment. This is especially true for a humanoid robot due to the very large number of redundant degrees of freedom and a large number of sensors that are required for a humanoid to work safely and effectively in the human environment. How can we address this fundamental problem? Motivated by human mental development from infancy to adulthood, we present a theory, an architecture, and some experimental results showing how to enable a robot to develop its mind automatically, through online, real time interactions with its environment. Humans mentally "raise" the robot through "robot sitting" and "robot schools" instead of task-specific robot programming.

International Journal of Humanoid Robotics, 2004

A hand-designed internal representation of the world cannot deal with unknown or uncontrolled environments. Motivated by human cognitive and behavioral development, this paper presents a theory, an architecture, and some experimental results for developmental robotics. By a developmental robot, we mean that the robot generates its "brain" (or "central nervous system," including the information processor and controller) through online, real-time interactions with its environment (including humans). A new Self-Aware Self-Effecting (SASE) agent concept is proposed, based on our SAIL and Dav developmental robots. The manual and autonomous development paradigms are formulated along with a theory of representation suited for autonomous development. Unlike traditional robot learning, the tasks that a developmental robot ends up learning are unknown during the programming time so that the task-specific representation must be generated and updated through real-time "...

Artificial Intelligence, 1994

Science, 2001

How does one create an intelligent machine? This problem has proven difficult. Over the past several decades, scientists have taken one of three approaches: In the first, which is knowledge-based, an intelligent machine in a laboratory is directly programmed to perform a given task. In a second, learning-based approach, a computer is "spoon-fed" human-edited sensory data while the machine is controlled by a task-specific learning program. Finally, by a "genetic search," robots have evolved through generations by the principle of survival of the fittest, mostly in a computer-simulated virtual world. Although notable, none of these is powerful enough to lead to machines having the complex, diverse, and highly integrated capabilities of an adult brain, such as vision, speech, and language. Nevertheless, these traditional approaches have served as the incubator for the birth and growth of a new direction for machine intelligence: autonomous mental development. As Kuhn wrote (1), "Failure of existing rules is the prelude to a search for new ones."

IEEE Transactions on Autonomous Mental Development, 2009

1998

Issued by Sandia National Laboratories, operated for the United States Department of Energy by San&a Corporation. NOTICE This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process &sclosed, or represents that its use would not infringe privately owned rights. Reference herein t o any specific commercial product, process, or service by trade name, trademark, manufacturer, o r otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government, any agency thereof, or any of their contractors or subcontractors. The views and opinions expressed herein do not necessarily state or reflect those of the United States Government, any agency thereof, or any of their contractors.