Proprioception and Imitation: On the Road to Agent Individuation View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

M. Lagarde , P. Andry , P. Gaussier , S. Boucenna , L. Hafemeister

ABSTRACT

In this paper, we will show that different kinds of interactive behaviors can emerge according to the kind of proprioceptive function available in a given sensori-motor system. We will study three different examples. In the first one, an internal proprioceptive signal is available for the learning of the visuo-motor coordination between an arm and a camera. An imitation behavior can emerge when the robot’s eye focuses on the hand of the experimenter instead of its own hand. The imitative behavior results from the error minimization between the visual signal and the proprioceptive signal. In the second example, we will show that similar modifications of the robot’s initial dynamics allows to learn some of the space-time properties of more complex behaviors under the form of a sequence of sensori-motor associations. In the third example, a robot head has to recognize the facial expression of the human caregiver. Yet, the robot has no visual feedback of its own facial expression. The human expressive resonance will allow the robot to select the visual features relevant for a particular facial expression. As a result, after few minutes of interactions, the robot can imitates the facial expression of the human partner. We will show that the different proprioceptive signals used in the examples can be seen as bootstrap mechanisms for more complex interactions. Applied as a crude model of the human, we will propose that these mechanisms play an important role in the process of individuation. More... »

PAGES

43-63

References to SciGraph publications

  • 2010. Learning to Imitate Human Actions through Eigenposes in FROM MOTOR LEARNING TO INTERACTION LEARNING IN ROBOTS
  • 2004-09. Can a robot empathize with people? in ARTIFICIAL LIFE AND ROBOTICS
  • 2007. The Role of Internal Oscillators for the One-Shot Learning of Complex Temporal Sequences in ARTIFICIAL NEURAL NETWORKS – ICANN 2007
  • 2002-10. Resonant spatiotemporal learning in large random recurrent networks in BIOLOGICAL CYBERNETICS
  • 2010. Abstraction Levels for Robotic Imitation: Overview and Computational Approaches in FROM MOTOR LEARNING TO INTERACTION LEARNING IN ROBOTS
  • 2009-07. Learning to search: Functional gradient techniques for imitation learning in AUTONOMOUS ROBOTS
  • 1999. A Neural Structure for Learning by Imitation in ADVANCES IN ARTIFICIAL LIFE
  • 1977-06. Dynamics of pattern formation in lateral-inhibition type neural fields in BIOLOGICAL CYBERNETICS
  • 2004. Toward a Cognitive System Algebra: Application to Facial Expression Learning and Imitation in EMBODIED ARTIFICIAL INTELLIGENCE
  • Book

    TITLE

    From Motor Learning to Interaction Learning in Robots

    ISBN

    978-3-642-05180-7
    978-3-642-05181-4

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-05181-4_3

    DOI

    http://dx.doi.org/10.1007/978-3-642-05181-4_3

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1051391752


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Information Processing and System Research Lab", 
              "id": "https://www.grid.ac/institutes/grid.463844.9", 
              "name": [
                "ETIS, ENSEA, Univ Cergy-pontoise, CNRS UMR 8051, F-95000, Cergy-Pontoise"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lagarde", 
            "givenName": "M.", 
            "id": "sg:person.014114521627.31", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014114521627.31"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Information Processing and System Research Lab", 
              "id": "https://www.grid.ac/institutes/grid.463844.9", 
              "name": [
                "ETIS, ENSEA, Univ Cergy-pontoise, CNRS UMR 8051, F-95000, Cergy-Pontoise"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Andry", 
            "givenName": "P.", 
            "id": "sg:person.012152621257.16", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012152621257.16"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "IUF"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Gaussier", 
            "givenName": "P.", 
            "id": "sg:person.01041272554.05", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041272554.05"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Information Processing and System Research Lab", 
              "id": "https://www.grid.ac/institutes/grid.463844.9", 
              "name": [
                "ETIS, ENSEA, Univ Cergy-pontoise, CNRS UMR 8051, F-95000, Cergy-Pontoise"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Boucenna", 
            "givenName": "S.", 
            "id": "sg:person.07730435642.64", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07730435642.64"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Information Processing and System Research Lab", 
              "id": "https://www.grid.ac/institutes/grid.463844.9", 
              "name": [
                "ETIS, ENSEA, Univ Cergy-pontoise, CNRS UMR 8051, F-95000, Cergy-Pontoise"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Hafemeister", 
            "givenName": "L.", 
            "id": "sg:person.010726607664.26", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010726607664.26"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1017/cbo9780511489808.012", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001044091"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-27833-7_18", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001253955", 
              "https://doi.org/10.1007/978-3-540-27833-7_18"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0926-6410(92)90003-a", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001794667"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0926-6410(92)90003-a", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1001794667"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/105971230401200203", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004786902"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/105971230401200203", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004786902"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-05181-4_15", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005576635", 
              "https://doi.org/10.1007/978-3-642-05181-4_15"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-05181-4_15", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005576635", 
              "https://doi.org/10.1007/978-3-642-05181-4_15"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0921-8890(95)00049-6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005616328"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1467-9450.1963.tb01326.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1006990773"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/088395198117596", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009321480"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-74690-4_95", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009776103", 
              "https://doi.org/10.1007/978-3-540-74690-4_95"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-540-74690-4_95", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009776103", 
              "https://doi.org/10.1007/978-3-540-74690-4_95"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/0278364902021010096", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013802171"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/0278364902021010096", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013802171"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.neunet.2009.01.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018351028"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/1362361300004002003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020953217"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/1362361300004002003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020953217"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1098/rstb.2002.1261", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026524765"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00422-002-0364-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1026775886", 
              "https://doi.org/10.1007/s00422-002-0364-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1037/0033-295x.95.1.49", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029502400"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-05181-4_14", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030429995", 
              "https://doi.org/10.1007/978-3-642-05181-4_14"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-05181-4_14", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030429995", 
              "https://doi.org/10.1007/978-3-642-05181-4_14"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10514-009-9121-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036163088", 
              "https://doi.org/10.1007/s10514-009-9121-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10514-009-9121-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036163088", 
              "https://doi.org/10.1007/s10514-009-9121-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10514-009-9121-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036163088", 
              "https://doi.org/10.1007/s10514-009-9121-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0166-4115(97)80121-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038179048"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10015-004-0293-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039090518", 
              "https://doi.org/10.1007/s10015-004-0293-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-48304-7_40", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041140190", 
              "https://doi.org/10.1007/3-540-48304-7_40"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-48304-7_40", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1041140190", 
              "https://doi.org/10.1007/3-540-48304-7_40"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00337259", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045614723", 
              "https://doi.org/10.1007/bf00337259"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00337259", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045614723", 
              "https://doi.org/10.1007/bf00337259"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0926-6410(02)00062-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048090989"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0028-3932(98)00006-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049531634"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1037/0735-7044.99.5.1006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049686709"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0893-6080(94)90092-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052031775"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0893-6080(94)90092-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052031775"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/3468.952717", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061157851"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1126/science.198.4312.75", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1062515860"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/robot.2000.846360", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093826163"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/ijcnn.2005.1555949", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094521781"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/roman.2003.1251814", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095508653"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2010", 
        "datePublishedReg": "2010-01-01", 
        "description": "In this paper, we will show that different kinds of interactive behaviors can emerge according to the kind of proprioceptive function available in a given sensori-motor system. We will study three different examples. In the first one, an internal proprioceptive signal is available for the learning of the visuo-motor coordination between an arm and a camera. An imitation behavior can emerge when the robot\u2019s eye focuses on the hand of the experimenter instead of its own hand. The imitative behavior results from the error minimization between the visual signal and the proprioceptive signal. In the second example, we will show that similar modifications of the robot\u2019s initial dynamics allows to learn some of the space-time properties of more complex behaviors under the form of a sequence of sensori-motor associations. In the third example, a robot head has to recognize the facial expression of the human caregiver. Yet, the robot has no visual feedback of its own facial expression. The human expressive resonance will allow the robot to select the visual features relevant for a particular facial expression. As a result, after few minutes of interactions, the robot can imitates the facial expression of the human partner. We will show that the different proprioceptive signals used in the examples can be seen as bootstrap mechanisms for more complex interactions. Applied as a crude model of the human, we will propose that these mechanisms play an important role in the process of individuation.", 
        "editor": [
          {
            "familyName": "Sigaud", 
            "givenName": "Olivier", 
            "type": "Person"
          }, 
          {
            "familyName": "Peters", 
            "givenName": "Jan", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-3-642-05181-4_3", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": {
          "isbn": [
            "978-3-642-05180-7", 
            "978-3-642-05181-4"
          ], 
          "name": "From Motor Learning to Interaction Learning in Robots", 
          "type": "Book"
        }, 
        "name": "Proprioception and Imitation: On the Road to Agent Individuation", 
        "pagination": "43-63", 
        "productId": [
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1051391752"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-3-642-05181-4_3"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "f4cf3df80334aaa20d7d8019916f67de648f970aa83925b5a91bc64dea2d1aa4"
            ]
          }
        ], 
        "publisher": {
          "location": "Berlin, Heidelberg", 
          "name": "Springer Berlin Heidelberg", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-3-642-05181-4_3", 
          "https://app.dimensions.ai/details/publication/pub.1051391752"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-16T07:29", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000356_0000000356/records_57868_00000000.jsonl", 
        "type": "Chapter", 
        "url": "https://link.springer.com/10.1007%2F978-3-642-05181-4_3"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-05181-4_3'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-05181-4_3'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-05181-4_3'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-05181-4_3'


     

    This table displays all metadata directly associated to this object as RDF triples.

    199 TRIPLES      23 PREDICATES      57 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-3-642-05181-4_3 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author Nefd48f55b5f24d5489143fe128a05e2d
    4 schema:citation sg:pub.10.1007/3-540-48304-7_40
    5 sg:pub.10.1007/978-3-540-27833-7_18
    6 sg:pub.10.1007/978-3-540-74690-4_95
    7 sg:pub.10.1007/978-3-642-05181-4_14
    8 sg:pub.10.1007/978-3-642-05181-4_15
    9 sg:pub.10.1007/bf00337259
    10 sg:pub.10.1007/s00422-002-0364-8
    11 sg:pub.10.1007/s10015-004-0293-9
    12 sg:pub.10.1007/s10514-009-9121-3
    13 https://doi.org/10.1016/0893-6080(94)90092-2
    14 https://doi.org/10.1016/0921-8890(95)00049-6
    15 https://doi.org/10.1016/0926-6410(92)90003-a
    16 https://doi.org/10.1016/j.neunet.2009.01.005
    17 https://doi.org/10.1016/s0028-3932(98)00006-2
    18 https://doi.org/10.1016/s0166-4115(97)80121-5
    19 https://doi.org/10.1016/s0926-6410(02)00062-9
    20 https://doi.org/10.1017/cbo9780511489808.012
    21 https://doi.org/10.1037/0033-295x.95.1.49
    22 https://doi.org/10.1037/0735-7044.99.5.1006
    23 https://doi.org/10.1080/088395198117596
    24 https://doi.org/10.1098/rstb.2002.1261
    25 https://doi.org/10.1109/3468.952717
    26 https://doi.org/10.1109/ijcnn.2005.1555949
    27 https://doi.org/10.1109/robot.2000.846360
    28 https://doi.org/10.1109/roman.2003.1251814
    29 https://doi.org/10.1111/j.1467-9450.1963.tb01326.x
    30 https://doi.org/10.1126/science.198.4312.75
    31 https://doi.org/10.1177/0278364902021010096
    32 https://doi.org/10.1177/105971230401200203
    33 https://doi.org/10.1177/1362361300004002003
    34 schema:datePublished 2010
    35 schema:datePublishedReg 2010-01-01
    36 schema:description In this paper, we will show that different kinds of interactive behaviors can emerge according to the kind of proprioceptive function available in a given sensori-motor system. We will study three different examples. In the first one, an internal proprioceptive signal is available for the learning of the visuo-motor coordination between an arm and a camera. An imitation behavior can emerge when the robot’s eye focuses on the hand of the experimenter instead of its own hand. The imitative behavior results from the error minimization between the visual signal and the proprioceptive signal. In the second example, we will show that similar modifications of the robot’s initial dynamics allows to learn some of the space-time properties of more complex behaviors under the form of a sequence of sensori-motor associations. In the third example, a robot head has to recognize the facial expression of the human caregiver. Yet, the robot has no visual feedback of its own facial expression. The human expressive resonance will allow the robot to select the visual features relevant for a particular facial expression. As a result, after few minutes of interactions, the robot can imitates the facial expression of the human partner. We will show that the different proprioceptive signals used in the examples can be seen as bootstrap mechanisms for more complex interactions. Applied as a crude model of the human, we will propose that these mechanisms play an important role in the process of individuation.
    37 schema:editor Nee045287b71444738cf7ae65a5afcb97
    38 schema:genre chapter
    39 schema:inLanguage en
    40 schema:isAccessibleForFree true
    41 schema:isPartOf N4fae5a837e0b40a38fa4e521841f0c91
    42 schema:name Proprioception and Imitation: On the Road to Agent Individuation
    43 schema:pagination 43-63
    44 schema:productId N2ddc059a03184724ad453dad701bd51a
    45 N6590dfca6c2b466ca2635646ae356d12
    46 N6cd79989b8cb41cd8ff24d84154aee21
    47 schema:publisher Nc3c62034634c4cffb26c02ff4b5ddcaa
    48 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051391752
    49 https://doi.org/10.1007/978-3-642-05181-4_3
    50 schema:sdDatePublished 2019-04-16T07:29
    51 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    52 schema:sdPublisher N31b2d735e38846f7b4f4bbfa15a92404
    53 schema:url https://link.springer.com/10.1007%2F978-3-642-05181-4_3
    54 sgo:license sg:explorer/license/
    55 sgo:sdDataset chapters
    56 rdf:type schema:Chapter
    57 N135c0111d40a4490be3d8c05cf89a2d8 rdf:first N4c34dd943a2542d9bbc1277c6386a897
    58 rdf:rest rdf:nil
    59 N2ddc059a03184724ad453dad701bd51a schema:name dimensions_id
    60 schema:value pub.1051391752
    61 rdf:type schema:PropertyValue
    62 N31b2d735e38846f7b4f4bbfa15a92404 schema:name Springer Nature - SN SciGraph project
    63 rdf:type schema:Organization
    64 N3bd8a0bc08e1430f9a50fa437d36d2c2 rdf:first sg:person.07730435642.64
    65 rdf:rest N8fd1b88985fc40d795dd070881eff173
    66 N4c34dd943a2542d9bbc1277c6386a897 schema:familyName Peters
    67 schema:givenName Jan
    68 rdf:type schema:Person
    69 N4fae5a837e0b40a38fa4e521841f0c91 schema:isbn 978-3-642-05180-7
    70 978-3-642-05181-4
    71 schema:name From Motor Learning to Interaction Learning in Robots
    72 rdf:type schema:Book
    73 N6590dfca6c2b466ca2635646ae356d12 schema:name readcube_id
    74 schema:value f4cf3df80334aaa20d7d8019916f67de648f970aa83925b5a91bc64dea2d1aa4
    75 rdf:type schema:PropertyValue
    76 N6cd79989b8cb41cd8ff24d84154aee21 schema:name doi
    77 schema:value 10.1007/978-3-642-05181-4_3
    78 rdf:type schema:PropertyValue
    79 N79288eeb148f4ebd912375335cffe374 rdf:first sg:person.01041272554.05
    80 rdf:rest N3bd8a0bc08e1430f9a50fa437d36d2c2
    81 N8fd1b88985fc40d795dd070881eff173 rdf:first sg:person.010726607664.26
    82 rdf:rest rdf:nil
    83 N9aaf18afaf1049c880cb1b2f2ffc67b5 schema:familyName Sigaud
    84 schema:givenName Olivier
    85 rdf:type schema:Person
    86 Na5da25d4abad4a0ab2d18dfd75abe84f schema:name IUF
    87 rdf:type schema:Organization
    88 Nc3c62034634c4cffb26c02ff4b5ddcaa schema:location Berlin, Heidelberg
    89 schema:name Springer Berlin Heidelberg
    90 rdf:type schema:Organisation
    91 Nee045287b71444738cf7ae65a5afcb97 rdf:first N9aaf18afaf1049c880cb1b2f2ffc67b5
    92 rdf:rest N135c0111d40a4490be3d8c05cf89a2d8
    93 Nefd48f55b5f24d5489143fe128a05e2d rdf:first sg:person.014114521627.31
    94 rdf:rest Nf64dfd61b26846c18f9298705353bd98
    95 Nf64dfd61b26846c18f9298705353bd98 rdf:first sg:person.012152621257.16
    96 rdf:rest N79288eeb148f4ebd912375335cffe374
    97 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    98 schema:name Information and Computing Sciences
    99 rdf:type schema:DefinedTerm
    100 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    101 schema:name Artificial Intelligence and Image Processing
    102 rdf:type schema:DefinedTerm
    103 sg:person.01041272554.05 schema:affiliation Na5da25d4abad4a0ab2d18dfd75abe84f
    104 schema:familyName Gaussier
    105 schema:givenName P.
    106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041272554.05
    107 rdf:type schema:Person
    108 sg:person.010726607664.26 schema:affiliation https://www.grid.ac/institutes/grid.463844.9
    109 schema:familyName Hafemeister
    110 schema:givenName L.
    111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010726607664.26
    112 rdf:type schema:Person
    113 sg:person.012152621257.16 schema:affiliation https://www.grid.ac/institutes/grid.463844.9
    114 schema:familyName Andry
    115 schema:givenName P.
    116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012152621257.16
    117 rdf:type schema:Person
    118 sg:person.014114521627.31 schema:affiliation https://www.grid.ac/institutes/grid.463844.9
    119 schema:familyName Lagarde
    120 schema:givenName M.
    121 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014114521627.31
    122 rdf:type schema:Person
    123 sg:person.07730435642.64 schema:affiliation https://www.grid.ac/institutes/grid.463844.9
    124 schema:familyName Boucenna
    125 schema:givenName S.
    126 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07730435642.64
    127 rdf:type schema:Person
    128 sg:pub.10.1007/3-540-48304-7_40 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041140190
    129 https://doi.org/10.1007/3-540-48304-7_40
    130 rdf:type schema:CreativeWork
    131 sg:pub.10.1007/978-3-540-27833-7_18 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001253955
    132 https://doi.org/10.1007/978-3-540-27833-7_18
    133 rdf:type schema:CreativeWork
    134 sg:pub.10.1007/978-3-540-74690-4_95 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009776103
    135 https://doi.org/10.1007/978-3-540-74690-4_95
    136 rdf:type schema:CreativeWork
    137 sg:pub.10.1007/978-3-642-05181-4_14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030429995
    138 https://doi.org/10.1007/978-3-642-05181-4_14
    139 rdf:type schema:CreativeWork
    140 sg:pub.10.1007/978-3-642-05181-4_15 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005576635
    141 https://doi.org/10.1007/978-3-642-05181-4_15
    142 rdf:type schema:CreativeWork
    143 sg:pub.10.1007/bf00337259 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045614723
    144 https://doi.org/10.1007/bf00337259
    145 rdf:type schema:CreativeWork
    146 sg:pub.10.1007/s00422-002-0364-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026775886
    147 https://doi.org/10.1007/s00422-002-0364-8
    148 rdf:type schema:CreativeWork
    149 sg:pub.10.1007/s10015-004-0293-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039090518
    150 https://doi.org/10.1007/s10015-004-0293-9
    151 rdf:type schema:CreativeWork
    152 sg:pub.10.1007/s10514-009-9121-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036163088
    153 https://doi.org/10.1007/s10514-009-9121-3
    154 rdf:type schema:CreativeWork
    155 https://doi.org/10.1016/0893-6080(94)90092-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052031775
    156 rdf:type schema:CreativeWork
    157 https://doi.org/10.1016/0921-8890(95)00049-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1005616328
    158 rdf:type schema:CreativeWork
    159 https://doi.org/10.1016/0926-6410(92)90003-a schema:sameAs https://app.dimensions.ai/details/publication/pub.1001794667
    160 rdf:type schema:CreativeWork
    161 https://doi.org/10.1016/j.neunet.2009.01.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018351028
    162 rdf:type schema:CreativeWork
    163 https://doi.org/10.1016/s0028-3932(98)00006-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049531634
    164 rdf:type schema:CreativeWork
    165 https://doi.org/10.1016/s0166-4115(97)80121-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038179048
    166 rdf:type schema:CreativeWork
    167 https://doi.org/10.1016/s0926-6410(02)00062-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048090989
    168 rdf:type schema:CreativeWork
    169 https://doi.org/10.1017/cbo9780511489808.012 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001044091
    170 rdf:type schema:CreativeWork
    171 https://doi.org/10.1037/0033-295x.95.1.49 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029502400
    172 rdf:type schema:CreativeWork
    173 https://doi.org/10.1037/0735-7044.99.5.1006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049686709
    174 rdf:type schema:CreativeWork
    175 https://doi.org/10.1080/088395198117596 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009321480
    176 rdf:type schema:CreativeWork
    177 https://doi.org/10.1098/rstb.2002.1261 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026524765
    178 rdf:type schema:CreativeWork
    179 https://doi.org/10.1109/3468.952717 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061157851
    180 rdf:type schema:CreativeWork
    181 https://doi.org/10.1109/ijcnn.2005.1555949 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094521781
    182 rdf:type schema:CreativeWork
    183 https://doi.org/10.1109/robot.2000.846360 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093826163
    184 rdf:type schema:CreativeWork
    185 https://doi.org/10.1109/roman.2003.1251814 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095508653
    186 rdf:type schema:CreativeWork
    187 https://doi.org/10.1111/j.1467-9450.1963.tb01326.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1006990773
    188 rdf:type schema:CreativeWork
    189 https://doi.org/10.1126/science.198.4312.75 schema:sameAs https://app.dimensions.ai/details/publication/pub.1062515860
    190 rdf:type schema:CreativeWork
    191 https://doi.org/10.1177/0278364902021010096 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013802171
    192 rdf:type schema:CreativeWork
    193 https://doi.org/10.1177/105971230401200203 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004786902
    194 rdf:type schema:CreativeWork
    195 https://doi.org/10.1177/1362361300004002003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020953217
    196 rdf:type schema:CreativeWork
    197 https://www.grid.ac/institutes/grid.463844.9 schema:alternateName Information Processing and System Research Lab
    198 schema:name ETIS, ENSEA, Univ Cergy-pontoise, CNRS UMR 8051, F-95000, Cergy-Pontoise
    199 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...