Diffusion-based learning theory for organizing visuo-motor coordination View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1998-10

AUTHORS

Zhiwei Luo, Masami Ito

ABSTRACT

A diffusion-based learning theory is presented and applied to organize the visuomotor coordination of an eye-hand system which has redundant motion degree of freedom (dof). This theory considers the spatial optimality of the coordination: to minimize the end-effector position error of the eye-hand system as well as the differentiation of the joint angles with respect to the end-effector positions over all the bounded work space. By introducing variational methods with respect to the space, we derive a partial differential equation (PDE) of the joint angles with respect to the work space. The equation includes a diffusion term. For the given boundary conditions and the initial conditions, it can be solved uniquely, and the solution is a well organized map. From the motor learning point of view, our approach contains both the aspects of supervised learning as well as self-organization. Firstly, we assume that the forward relation from the hand system's joint angles to its end-effector positions can be obtained using supervised learning, and at the boundary of the work space, the supervisor can provide correct joint information. Then, by evolving the diffusion equation, we organize the visuomotor coordination. We show the effectiveness of this approach using a 3-dof scale manipulator. The problems of how to realize the visuomotor map; how to utilize the resultant map in several motions; and what are the influences of the initial conditions on the map formation and the relation to the boundary conditions are also discussed using computer simulations. Our approach has three advantages: (1) it does not require too many trial motions for the eye-hand system; (2) during the map formation process, it requires only the local interactions between each node; and (3) it guarantees the final map's spatial optimality over all the bounded work space. More... »

PAGES

279-289

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s004220050478

DOI

http://dx.doi.org/10.1007/s004220050478

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1001280440

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/9830703


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"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computer Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Learning", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Neurological", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Movement", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Psychomotor Performance", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Space Perception", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "RIKEN", 
          "id": "https://www.grid.ac/institutes/grid.7597.c", 
          "name": [
            "Bio-Mimetic Control Research Center, the Institute of Physical and Chemical Research (RIKEN), Anagahora, Shimoshidami,  Moriyama-ku, Nagoya, 463-0003 Japan, JP"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Luo", 
        "givenName": "Zhiwei", 
        "id": "sg:person.016453442516.40", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016453442516.40"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "RIKEN", 
          "id": "https://www.grid.ac/institutes/grid.7597.c", 
          "name": [
            "Bio-Mimetic Control Research Center, the Institute of Physical and Chemical Research (RIKEN), Anagahora, Shimoshidami,  Moriyama-ku, Nagoya, 463-0003 Japan, JP"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ito", 
        "givenName": "Masami", 
        "id": "sg:person.013037773365.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013037773365.25"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "1998-10", 
    "datePublishedReg": "1998-10-01", 
    "description": "A diffusion-based learning theory is presented and applied to organize the visuomotor coordination of an eye-hand system which has redundant motion degree of freedom (dof). This theory considers the spatial optimality of the coordination: to minimize the end-effector position error of the eye-hand system as well as the differentiation of the joint angles with respect to the end-effector positions over all the bounded work space. By introducing variational methods with respect to the space, we derive a partial differential equation (PDE) of the joint angles with respect to the work space. The equation includes a diffusion term. For the given boundary conditions and the initial conditions, it can be solved uniquely, and the solution is a well organized map. From the motor learning point of view, our approach contains both the aspects of supervised learning as well as self-organization. Firstly, we assume that the forward relation from the hand system's joint angles to its end-effector positions can be obtained using supervised learning, and at the boundary of the work space, the supervisor can provide correct joint information. Then, by evolving the diffusion equation, we organize the visuomotor coordination. We show the effectiveness of this approach using a 3-dof scale manipulator. The problems of how to realize the visuomotor map; how to utilize the resultant map in several motions; and what are the influences of the initial conditions on the map formation and the relation to the boundary conditions are also discussed using computer simulations. Our approach has three advantages: (1) it does not require too many trial motions for the eye-hand system; (2) during the map formation process, it requires only the local interactions between each node; and (3) it guarantees the final map's spatial optimality over all the bounded work space.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/s004220050478", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1081741", 
        "issn": [
          "0340-1200", 
          "1432-0770"
        ], 
        "name": "Biological Cybernetics", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "79"
      }
    ], 
    "name": "Diffusion-based learning theory for organizing visuo-motor coordination", 
    "pagination": "279-289", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "0619531e00b0785da854dd611238ec9ca149a5d7fc91fbc8c1e386d3787bca0b"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "9830703"
        ]
      }, 
      {
        "name": "nlm_unique_id", 
        "type": "PropertyValue", 
        "value": [
          "7502533"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s004220050478"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1001280440"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s004220050478", 
      "https://app.dimensions.ai/details/publication/pub.1001280440"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-10T19:02", 
    "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/v90/0000000001_0000000264/records_8678_00000485.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/s004220050478"
  }
]
 

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/s004220050478'

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/s004220050478'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s004220050478'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s004220050478'


 

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

104 TRIPLES      20 PREDICATES      36 URIs      28 LITERALS      16 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s004220050478 schema:about N0809b5754d944ed0babcc62b7d5e9523
2 N149d965b1f9d4efe910ee9807392c1b5
3 N19a6eb4e18c54df697bd92d2c6091ffa
4 N31dd782131b74af0937f4d0fc6dad1e6
5 N3e82487a525342d8b18ee708c1f593cd
6 N9ada28278a944d0480f4cff9fcaec794
7 Nad19f8bf0dfc47c096862348b47dcace
8 anzsrc-for:08
9 anzsrc-for:0801
10 schema:author N5285552d8e264952b34d3c812288263b
11 schema:datePublished 1998-10
12 schema:datePublishedReg 1998-10-01
13 schema:description A diffusion-based learning theory is presented and applied to organize the visuomotor coordination of an eye-hand system which has redundant motion degree of freedom (dof). This theory considers the spatial optimality of the coordination: to minimize the end-effector position error of the eye-hand system as well as the differentiation of the joint angles with respect to the end-effector positions over all the bounded work space. By introducing variational methods with respect to the space, we derive a partial differential equation (PDE) of the joint angles with respect to the work space. The equation includes a diffusion term. For the given boundary conditions and the initial conditions, it can be solved uniquely, and the solution is a well organized map. From the motor learning point of view, our approach contains both the aspects of supervised learning as well as self-organization. Firstly, we assume that the forward relation from the hand system's joint angles to its end-effector positions can be obtained using supervised learning, and at the boundary of the work space, the supervisor can provide correct joint information. Then, by evolving the diffusion equation, we organize the visuomotor coordination. We show the effectiveness of this approach using a 3-dof scale manipulator. The problems of how to realize the visuomotor map; how to utilize the resultant map in several motions; and what are the influences of the initial conditions on the map formation and the relation to the boundary conditions are also discussed using computer simulations. Our approach has three advantages: (1) it does not require too many trial motions for the eye-hand system; (2) during the map formation process, it requires only the local interactions between each node; and (3) it guarantees the final map's spatial optimality over all the bounded work space.
14 schema:genre research_article
15 schema:inLanguage en
16 schema:isAccessibleForFree false
17 schema:isPartOf N29cf32ac687b40c487f9865643daf2b1
18 Nac5bdf78ebc442ed879946d39cce8e92
19 sg:journal.1081741
20 schema:name Diffusion-based learning theory for organizing visuo-motor coordination
21 schema:pagination 279-289
22 schema:productId N8474e2477c39472196d372cb2c97aee8
23 N9679b2b54e25492995e502ad671c368c
24 Nbb5544026802480db4f2625fbf2706f7
25 Nbd598481c8d147f2bacc6199b27ca96d
26 Nfbe672f1915e4a9aba8c371f49bd338e
27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001280440
28 https://doi.org/10.1007/s004220050478
29 schema:sdDatePublished 2019-04-10T19:02
30 schema:sdLicense https://scigraph.springernature.com/explorer/license/
31 schema:sdPublisher Nba19bdbbe0ea409a9ac7d8f5089a5cf6
32 schema:url http://link.springer.com/10.1007/s004220050478
33 sgo:license sg:explorer/license/
34 sgo:sdDataset articles
35 rdf:type schema:ScholarlyArticle
36 N0809b5754d944ed0babcc62b7d5e9523 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
37 schema:name Learning
38 rdf:type schema:DefinedTerm
39 N149d965b1f9d4efe910ee9807392c1b5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
40 schema:name Psychomotor Performance
41 rdf:type schema:DefinedTerm
42 N19a6eb4e18c54df697bd92d2c6091ffa schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
43 schema:name Space Perception
44 rdf:type schema:DefinedTerm
45 N29cf32ac687b40c487f9865643daf2b1 schema:volumeNumber 79
46 rdf:type schema:PublicationVolume
47 N31dd782131b74af0937f4d0fc6dad1e6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
48 schema:name Movement
49 rdf:type schema:DefinedTerm
50 N3e82487a525342d8b18ee708c1f593cd schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
51 schema:name Computer Simulation
52 rdf:type schema:DefinedTerm
53 N5285552d8e264952b34d3c812288263b rdf:first sg:person.016453442516.40
54 rdf:rest N7cf7f6c795aa497ca5571054e55b1847
55 N7cf7f6c795aa497ca5571054e55b1847 rdf:first sg:person.013037773365.25
56 rdf:rest rdf:nil
57 N8474e2477c39472196d372cb2c97aee8 schema:name doi
58 schema:value 10.1007/s004220050478
59 rdf:type schema:PropertyValue
60 N9679b2b54e25492995e502ad671c368c schema:name nlm_unique_id
61 schema:value 7502533
62 rdf:type schema:PropertyValue
63 N9ada28278a944d0480f4cff9fcaec794 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
64 schema:name Humans
65 rdf:type schema:DefinedTerm
66 Nac5bdf78ebc442ed879946d39cce8e92 schema:issueNumber 4
67 rdf:type schema:PublicationIssue
68 Nad19f8bf0dfc47c096862348b47dcace schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
69 schema:name Models, Neurological
70 rdf:type schema:DefinedTerm
71 Nba19bdbbe0ea409a9ac7d8f5089a5cf6 schema:name Springer Nature - SN SciGraph project
72 rdf:type schema:Organization
73 Nbb5544026802480db4f2625fbf2706f7 schema:name readcube_id
74 schema:value 0619531e00b0785da854dd611238ec9ca149a5d7fc91fbc8c1e386d3787bca0b
75 rdf:type schema:PropertyValue
76 Nbd598481c8d147f2bacc6199b27ca96d schema:name dimensions_id
77 schema:value pub.1001280440
78 rdf:type schema:PropertyValue
79 Nfbe672f1915e4a9aba8c371f49bd338e schema:name pubmed_id
80 schema:value 9830703
81 rdf:type schema:PropertyValue
82 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
83 schema:name Information and Computing Sciences
84 rdf:type schema:DefinedTerm
85 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
86 schema:name Artificial Intelligence and Image Processing
87 rdf:type schema:DefinedTerm
88 sg:journal.1081741 schema:issn 0340-1200
89 1432-0770
90 schema:name Biological Cybernetics
91 rdf:type schema:Periodical
92 sg:person.013037773365.25 schema:affiliation https://www.grid.ac/institutes/grid.7597.c
93 schema:familyName Ito
94 schema:givenName Masami
95 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013037773365.25
96 rdf:type schema:Person
97 sg:person.016453442516.40 schema:affiliation https://www.grid.ac/institutes/grid.7597.c
98 schema:familyName Luo
99 schema:givenName Zhiwei
100 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016453442516.40
101 rdf:type schema:Person
102 https://www.grid.ac/institutes/grid.7597.c schema:alternateName RIKEN
103 schema:name Bio-Mimetic Control Research Center, the Institute of Physical and Chemical Research (RIKEN), Anagahora, Shimoshidami, Moriyama-ku, Nagoya, 463-0003 Japan, JP
104 rdf:type schema:Organization
 




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


...