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Behavior-Based Systems

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Book cover Springer Handbook of Robotics

Part of the book series: Springer Handbooks ((SHB))

Abstract

Nature is filled with examples of autonomous creatures capable of dealing with the diversity, unpredictability, and rapidly changing conditions of the real world. Such creatures must make decisions and take actions based on incomplete perception, time constraints, limited knowledge about the world, cognition, reasoning and physical capabilities, in uncontrolled conditions and with very limited cues about the intent of others. Consequently, one way of evaluating intelligence is based on the creature’s ability to make the most of what it has available to handle the complexities of the real world. The main objective of this chapter is to explain behavior-based systems and their use in autonomous control problems and applications. The chapter is organized as follows. Section 13.1 overviews robot control, introducing behavior-based systems in relation to other established approaches to robot control. Section 13.2 follows by outlining the basic principles of behavior-based systems that make them distinct from other types of robot control architectures. The concept of basis behaviors, the means of modularizing behavior-based systems, is presented in Sect. 13.3. Section 13.4 describes how behaviors are used as building blocks for creating representations for use by behavior-based systems, enabling the robot to reason about the world and about itself in that world. Section 13.5 presents several different classes of learning methods for behavior-based systems, validated on single-robot and multi-robot systems. Section 13.6 provides an overview of various robotics problems and application domains that have successfully been addressed or are currently being studied with behavior-based control. Finally, Sect. 13.7 concludes the chapter.

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Abbreviations

AAAI:

American Association for Artificial Intelligence

APOC:

allowing dynamic selection and changes

AuRA:

autonomous robot architecture

BLE:

broadcast of local eligibility

BP:

behavior primitive

CEC:

Congress on Evolutionary Computation

DEA:

differential elastic actuator

EMIB:

emotion, motivation and intentional behavior

FP:

fusion primitive

HBBA:

hybrid behavior-based architecture

HRI:

human–robot interaction

IRL:

in real life

MBA:

motivated behavioral architecture

MVERT:

move value estimation for robot teams

RL:

reinforcement learning

SLAM:

simultaneous localization and mapping

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Authors and Affiliations

  1. Department of Electrical Engineering and Computer Engineering, University of Sherbrooke, 2500 Boul. Université, J1N4E5, Sherbrooke, Canada

    François Michaud

  2. Department of Computer Science and Engineering, University of Nevada, 1664 North Virginia Street, MS 171, NV 8955, Reno, USA

    Monica Nicolescu

Authors
  1. François Michaud
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  2. Monica Nicolescu
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Correspondence to François Michaud .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy

    Bruno Siciliano

  2. Department of Computer Sciences, Artificial Intelligence Laboratory, Stanford University, 450 Serra Mall, CA 94305, Stanford, USA

    Oussama Khatib

Video-References

Video-References

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Experience-based learning of high-level task representations: Demonstration available from http://handbookofrobotics.org/view-chapter/13/videodetails/27

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Experience-based learning of high-level task representations: Reproduction available from http://handbookofrobotics.org/view-chapter/13/videodetails/28

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Experience-based learning of high-level task representations: Demonstration (2) available from http://handbookofrobotics.org/view-chapter/13/videodetails/30

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Experience-based learning of high-level task representations: Reproduction (2) available from http://handbookofrobotics.org/view-chapter/13/videodetails/31

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Experience-based learning of high-level task representations: Demonstration (3) available from http://handbookofrobotics.org/view-chapter/13/videodetails/32

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Experience-based learning of high-level task representations: Reproduction (3) available from http://handbookofrobotics.org/view-chapter/13/videodetails/33

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The Nerd Herd available from http://handbookofrobotics.org/view-chapter/13/videodetails/34

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Toto available from http://handbookofrobotics.org/view-chapter/13/videodetails/35

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SpartacUS available from http://handbookofrobotics.org/view-chapter/13/videodetails/417

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Natural interaction design of a humanoid robot available from http://handbookofrobotics.org/view-chapter/13/videodetails/418

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Using ROS4iOS available from http://handbookofrobotics.org/view-chapter/13/videodetails/419

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Michaud, F., Nicolescu, M. (2016). Behavior-Based Systems. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer Handbooks. Springer, Cham. https://doi.org/10.1007/978-3-319-32552-1_13

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