Resilient Machines Through Continuous Self-Modeling

Animals sustain the ability to operate after injury by creating qualitatively different compensatory behaviors. Although such robustness would be desirable in engineered systems, most machines fail in the face of unexpected damage. We describe a robot that can recover from such change autonomously,...

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Bibliographic Details
Published in:Science (American Association for the Advancement of Science) 2006-11, Vol.314 (5802), p.1118-1121
Main Authors: Bongard, Josh, Zykov, Victor, Lipson, Hod
Format: Article
Language:English
Subjects:
Algorithms
Animals
Applied sciences
Behavior
Cognition
Cognition & reasoning
Computer science
control theory
systems
Computer Simulation
Control theory. Systems
Damage
Exact sciences and technology
Gait
Injuries
Locomotion
Machinery
Mathematical models
Modeling
Physical damage
Psychology
Robotics
Robots
Robustness
Sensors
Sheds
Topology
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Description
Summary:Animals sustain the ability to operate after injury by creating qualitatively different compensatory behaviors. Although such robustness would be desirable in engineered systems, most machines fail in the face of unexpected damage. We describe a robot that can recover from such change autonomously, through continuous self-modeling. A four-legged machine uses actuation-sensation relationships to indirectly infer its own structure, and it then uses this self-model to generate forward locomotion. When a leg part is removed, it adapts the self-models, leading to the generation of alternative gaits. This concept may help develop more robust machines and shed light on self-modeling in animals.
ISSN:0036-8075
1095-9203
DOI:10.1126/science.1133687