Deep kinematic inference affords efficient and scalable control of bodily movements

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. Inst...

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Veröffentlicht in:Proceedings of the National Academy of Sciences - PNAS Jg. 120; H. 51; S. e2309058120
Hauptverfasser: Priorelli, Matteo, Pezzulo, Giovanni, Stoianov, Ivilin Peev
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States 19.12.2023
Schlagworte:
Algorithms
Biomechanical Phenomena
Humans
Motivation
Movement
kinematics
active inference
predictive coding
motor control
neurocomputational modeling
ISSN:1091-6490, 1091-6490
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Zusammenfassung: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. We introduce 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.
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ISSN:1091-6490
1091-6490
DOI:10.1073/pnas.2309058120