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Cited by 549

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network

Brooks, Rodney A.·1989·Neural Computation

Abstract

Most animals have significant behavioral expertise built in without having to explicitly learn it all from scratch. This expertise is a product of evolution of the organism; it can be viewed as a very long-term form of learning which provides a structured system within which individuals might learn more specialized skills or abilities. This paper suggests one possible mechanism for analagous robot evolution by describing a carefully designed series of networks, each one being a strict augmentation of the previous one, which control a six-legged walking machine capable of walking over rough terrain and following a person passively sensed in the infrared spectrum. As the completely decentralized networks are augmented, the robot's performance and behavior repertoire demonstrably improve. The rationale for such demonstrations is that they may provide a hint as to the requirements for automatically building massive networks to carry out complex sensory-motor tasks. The experiments with an actual robot ensure that an essence of reality is maintained and that no critical disabling problems have been ignored.

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Crossref · 10.1162/neco.1989.1.2.253

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Metadata

Authors
Brooks, R. A.
Venue
Neural Computation
Published
1989-06
Volume
1
Issue
2
Pages
253-262
ISSN
  • 0899-7667 (ISSN-L)
  • 1530-888X
OpenAlex
W2624516165
Cited by
549
References
6
FWCI
19.58
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network. https://app.cochat.ai/papers/10.1162/neco.1989.1.2.253
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Brooks, R. A. (1989). A Robot that Walks; Emergent Behaviors from a Carefully Evolved Network. Neural Computation. https://doi.org/10.1162/neco.1989.1.2.253

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.