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Deep kinematic inference affords efficient and scalable control of bodily movements.

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Bibliographic Details
Title: Deep kinematic inference affords efficient and scalable control of bodily movements.
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]
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Database: Engineering Source
Description
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]
ISSN:00278424
DOI:10.1073/pnas.2309058120