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Behavior-Based Robotics

A Basis in Biology

Behavior-based robotics uses biology as the best model for understanding intelligence. Most roboticists do not model biological organisms directly, but rather look to nature for insight and direction. Increasingly, researchers have adopted the notion that high-level cognition is an impractical, debilitating goal, and have begun to model the lower animal world. While there is a definite danger of trying to stretch metaphors too thin, the fact is that biological models offer our best hope for creating adaptive behavior.

Photo of Ariel. ARIEL: a behavior-based robot developed by iRobot to locate and detonate mines within the surf-zone.

Biology serves not only as inspiration for underlying methodologies, but also for actual robot hardware and sensors. At the Centre National de la Recherche Scientifique in France, researchers discovered that a simple household fly navigates using a compound eye comprised of 3,000 facets which operate in parallel to monitor visual motion. In response, roboticists built an artificial robot eye with 100 facets that can provide a 360-degree panoramic view. (Pranceshini, Pichon and Blanes 1992) Artificial bees can simulate the dance patterns and sounds of real bees sufficiently well to actually communicate with other bees. (Kirchner & Town 1994) Others have managed to build robot cockroaches (Quinn and Espenschied 1993) and even ants capable of leaving and detecting pheromone trails. (Russell, Thiel, & Mackay-Sim 1994).

Photo of INAT. INAT: A robot developed at the Idaho National Laboratory which uses learned responses to light and sound fluctuations to modulate swarming behavior.

It is possible to view these successes as evidence supporting the behavior-based approach. In other words, if most animals do not rely on cognition to act, why should robotics? Roboticists’ preoccupation with high-level semantic thought merely reflects the anthropomorphic bias of human designers. To better understand the behavioral architecture of a low-level animal, scientists severed the connection between a frog’s spine and brain. The goal was to remove all centralized control so that all action was produced reactively and without “thought.” Scientists stimulated particular points along the spinal cord and found much of the behavior of a frog was encoded directly into the spine. There are twenty locations along the spine, each of which can react with a different, essential motion. Stimulating one location will prompt the frog to wipe its head whereas another will cause it to jump. If the spine is stimulated in two points simultaneously it is possible to combine behaviors and produce a more complex form of behavior. (Bizzi, Mussa-Ivaldi, & Giszter 1991)

Photo of DART. DART: An aquatic robot developed by iRobot.

This finding bears out a fundamental premise of the behavior-based approach: that sophisticated, high-level behavior can emerge from layered combinations of simple stimulus-response mappings. Instead of careful planning based on modeling, high-level behavior such as flocking or foraging can be built by blending low-level behaviors such as dispersion, aggregation, homing and wandering. Strategies can be built directly from behaviors, whereas plans must be based on an accurate model.

Of course, it is not only Biology which supplies insight to the field of robotics. As multi-disciplinary approaches become more prevalent, inspiration should flow freely between robotics, neuroscience, psychology, cognitive science, and a host of other fields.


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