Deep kinematic inference affords efficient and scalable control of bodily movements.
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| Title: | Deep kinematic inference affords efficient and scalable control of bodily movements. |
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| Authors: | Priorelli, Matteo1, Pezzulo, Giovanni2, Stoianov, Ivilin Peev1 ivilinpeev.stoianov@cnr.it |
| Source: | Proceedings of the National Academy of Sciences of the United States of America. 12/19/2023, Vol. 120 Issue 51, p1-9. 24p. |
| Subjects: | Kinematic chains, Cost functions, Human body |
| Abstract: | Performing goal-directed movements requires mapping goals from extrinsic (workspace-relative) to intrinsic (body-relative) coordinates and then to motor signals. Mainstream approaches based on optimal control realize the mappings by minimizing cost functions, which is computationally demanding. Instead, active inference uses generative models to produce sensory predictions, which allows a cheaper inversion to the motor signals. However, devising generative models to control complex kinematic chains like the human body is challenging.Weintroduce an active inference architecture that affords a simple but effective mapping from extrinsic to intrinsic coordinates via inference and easily scales up to drive complex kinematic chains. Rich goals can be specified in both intrinsic and extrinsic coordinates using attractive or repulsive forces. The proposed model reproduces sophisticated bodily movements and paves the way for computationally efficient and biologically plausible control of actuated systems. [ABSTRACT FROM AUTHOR] |
| Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 174430733 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Deep kinematic inference affords efficient and scalable control of bodily movements. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Priorelli%2C+Matteo%22">Priorelli, Matteo</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Pezzulo%2C+Giovanni%22">Pezzulo, Giovanni</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Stoianov%2C+Ivilin+Peev%22">Stoianov, Ivilin Peev</searchLink><relatesTo>1</relatesTo><i> ivilinpeev.stoianov@cnr.it</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Proceedings+of+the+National+Academy+of+Sciences+of+the+United+States+of+America%22">Proceedings of the National Academy of Sciences of the United States of America</searchLink>. 12/19/2023, Vol. 120 Issue 51, p1-9. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Kinematic+chains%22">Kinematic chains</searchLink><br /><searchLink fieldCode="DE" term="%22Cost+functions%22">Cost functions</searchLink><br /><searchLink fieldCode="DE" term="%22Human+body%22">Human body</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Performing goal-directed movements requires mapping goals from extrinsic (workspace-relative) to intrinsic (body-relative) coordinates and then to motor signals. Mainstream approaches based on optimal control realize the mappings by minimizing cost functions, which is computationally demanding. Instead, active inference uses generative models to produce sensory predictions, which allows a cheaper inversion to the motor signals. However, devising generative models to control complex kinematic chains like the human body is challenging.Weintroduce an active inference architecture that affords a simple but effective mapping from extrinsic to intrinsic coordinates via inference and easily scales up to drive complex kinematic chains. Rich goals can be specified in both intrinsic and extrinsic coordinates using attractive or repulsive forces. The proposed model reproduces sophisticated bodily movements and paves the way for computationally efficient and biologically plausible control of actuated systems. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Proceedings of the National Academy of Sciences of the United States of America is the property of National Academy of Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1073/pnas.2309058120 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1 Subjects: – SubjectFull: Kinematic chains Type: general – SubjectFull: Cost functions Type: general – SubjectFull: Human body Type: general Titles: – TitleFull: Deep kinematic inference affords efficient and scalable control of bodily movements. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Priorelli, Matteo – PersonEntity: Name: NameFull: Pezzulo, Giovanni – PersonEntity: Name: NameFull: Stoianov, Ivilin Peev IsPartOfRelationships: – BibEntity: Dates: – D: 19 M: 12 Text: 12/19/2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00278424 Numbering: – Type: volume Value: 120 – Type: issue Value: 51 Titles: – TitleFull: Proceedings of the National Academy of Sciences of the United States of America Type: main |
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