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Robots for Education

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Springer Handbook of Robotics

Abstract

This chapter provides an overview of the key ingredients that make successful education robots possible. Two very popular outlets for public interaction with robots are the robot tournament and the informal learning venue (e.g., the science museum). Section 55.2 provides a survey of the very popular world of robot-themed tournaments, which have already impacted tens of thousands of students across diverse geographic and age group boundaries [55.1,2,3,4,5]. Section 55.5 provides an overview of robotic installations in informal learning spaces. Robotic technology has now proven to have sufficient robustness and engagement to be a principal component of interactive exhibitry for a new generation of hands-on, active-learning museums [55.6].

To make interactive, educational robots successful, a new level of technology robustness and standardization is required, and significant progress has been made on this front in the past decade. Educational robot devices consist of both hardware (preassembled or as kits or components) and software (both as source code and programming environments). Section 55.3 discusses physical robot platforms that have achieved notable success, while Sect. 55.4 describes both low-level controllers that interface those platforms to high-level computation, as well as the top-level programming environments themselves.

Finally, an important class of tool in the study and execution of educational robotic systems is the ability to evaluate the efficacy of a robot system formally in an educational context. Numerous tools from human–computer interaction, cognitive psychology, and education have demonstrated their usefulness in this regard. Section 55.6 summarizes the manner in which conventional analytical tools may be used to evaluate unconventional educational programs that tap robotic technologies as learning tools across a variety of ages and in both formal and informal learning venues.

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Abbreviations

AAAI:

American Association for Artificial Intelligence

AI:

artificial intelligence

ANSI:

American National Standards Institute

ASD:

autism spectrum disorder

DIO:

digital input-output

EPFL:

Ecole Polytechnique Fédérale de Lausanne

FSM:

finite-state machine

HCI:

human computer interaction

I/O:

input/output

IEEE:

Institute of Electrical and Electronics Engineers

LAAS:

Laboratoire dʼAnalyse et dʼArchitecture des Systèmes

NASA:

National Aeronautics and Space Agency

NCER:

National Conference on Educational Robotics

PC:

Purkinje cells

PC:

principal contact

PIC:

programmable intelligent computer

PIC:

programmable interrupt controller

PID:

proportional–integral–derivative

PPRK:

palm pilot robot kit

RAM:

random-access (volatile) memory

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Authors and Affiliations

  1. School of Aerospace and Mechanical Engineering, University of Oklahoma, 865 Asp Ave., 73019, Norman, OK, USA

    David P. Miller Prof

  2. The Robotics Institute, Carnegie Mellon University, 5000 Forbes Ave., 15213, Pittsburgh, PA, USA

    Illah R. Nourbakhsh Prof

  3. Department of Mechanical and Process Engineering, ETH Zentrum, Tannenstr. 3, CLA E32, 8092, Zürich, Switzerland

    Roland Siegwart Prof

Authors
  1. David P. Miller Prof
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  2. Illah R. Nourbakhsh Prof
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  3. Roland Siegwart Prof
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Corresponding authors

Correspondence to David P. Miller Prof , Illah R. Nourbakhsh Prof or Roland Siegwart Prof .

Editor information

Editors and Affiliations

  1. PRISMA Lab, Dipartimento di Informatica e Sistemistica, Universitá degli Studi di Napoli Federico II, 80125, Napoli, Italy, Via Claudio 21

    Bruno Siciliano Prof.

  2. Artificial Intelligence Laboratory, Department of Computer Science, Stanford University, 94305-9010, Stanford, CA, USA

    Oussama Khatib Prof.

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Miller, D.P., Nourbakhsh, I.R., Siegwart, R. (2008). Robots for Education. In: Siciliano, B., Khatib, O. (eds) Springer Handbook of Robotics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30301-5_56

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  • DOI: https://doi.org/10.1007/978-3-540-30301-5_56

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