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Author : RODNEY A. BROOKS
Artificial Intelligence Laboratory,
Massachusetts Institute of Technology In order to build Autonomous robots that can carry out useful work in unstructured environments new approaches have been developed to building intelligent systems. The relationship to traditional academic Robotics and traditional artificial intelligence is examined. In the new approaches a tight coupling of sensing to action produces architectures for intelligence that are networks of simple computational elements which are quite broad, but not very deep. Recent work within this approach has demonstrated the use of representations, expectations, plans, goals, and learning, but without resorting to the traditional uses, of central, abstractly manipulable or symbolic representations. Perception within these systems is often an active process, and the dynamics of the interactions with the world are extremely important. The question of how to evaluate and compare the new to traditional work still provokes vigorous discussion. THE FIELD OF ARTIFICIAL INTELLIGENCE (AI) tries to make computers do things that, when done by people, are described as having indicated intelligence. The goal of AI has been characterized as both the construction of useful intelligent systems and the understanding of human intelligence (1). Since AI's earliest days (2) there have been thoughts of building truly intelligent autonomous robots. In academic research circles, work in robotics has influenced work in Al and vice versa (3). Over the last 7 years a new approach to robotics has been developing in a number of laboratories. Rather than modularize perception, world modeling, planning, and execution, the new approach builds intelligent Control Systems where many individual modules each directly generate some part of the behavior of the robot. In the purest form of this model each module incorporates its own perceptual, modeling, and planning requirements. An arbitration or mediation scheme, built within the framework of the modules, Controls which behavior-producing module has control of which part of the Robot at any given time. The work draws its inspirations from neurobiology, ethology, psychophysics, and sociology. The approach grew out of dissatisfactions with traditional robotics and Al, which seemed unable to deliver real-time performance in a dynamic world. The key idea of the new approach is to advance both robotics and Al by considering the problems of building an autonomous agent that physically is an autonomous Mobile Robot and that carries out some useful tasks in an environment that has not been specially structured or engineered for it. There are two subtly different central ideas that are crucial and have led to solutions that use behavior-producing modules: - Situatedness: The Robots are situated in the world-they do not deal with abstract descriptions, but with the "here" and "now" of the environment that directly influences the behavior of the system.
- Embodiment: The robots have bodies and experience the world directly-their actions are part of a dynamic with the world, and the actions have immediate feedback on the robots' own sensations.
An airline reservation system is situated but it is not embodied-it deals with thousands of request per second, and its responses vary as its database changes, but it interacts with the world only through sending and receiving messages. A current generation industrial spray-painting robot is embodied but it is not situated-it has a physical extent and its servo routines must correct for its interactions with gravity and noise present in the system, but it does not perceive any aspects of the shape of an object presented to it for painting and simply goes through a pre-programmed series of actions. This new approach to robotics makes claims on how intelligence should be organized that are radically different from the approach assumed by traditional AI. Traditional Approaches
Although the fields of computer vision, robotics, and AI all have their fairly separate conferences and specialty journals, an implicit intellectual pact between them has developed over the years. None of these fields is experimental science in the sense that chemistry, for example, can be an experimental science. Rather, there are two ways in which the fields proceed. One is through the development and synthesis of models of aspects of perception, intelligence, or action, and the other is through the construction of demonstration systems (4). It is relatively rare for an explicit experiment to be done. Rather, the demonstration systems are used to illustrate a particular model in operation. There is no control experiment to compare against, and very little quantitative data extraction or analysis. The intellectual pact between computer vision, robotics, and AI concerns the assumptions that can be made in building demonstration systems. It establishes conventions for what the components of an eventual fully situated and embodied system can assume about each other. These conventions match those used in two critical projects from 1969 to 1972 which set the tone for the next 20 years of research in computer vision, robotics, and AI.
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