ArticleProceedings of the National Academy of Sciences of the United States of America2023
Deep kinematic inference affords efficient and scalable control of bodily movements.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Visual awareness of stimulus features shapes motor control through action end-state comfort.Scientific reports · 2026Article
- Editorial: Neural mechanisms of sensory-motor integration in manual tasks.Frontiers in psychology · 2026Article
- From scribbling to painting: children's drawing as an evolutionary laboratory for multisensory and sensorimotor integration.Frontiers in human neuroscience · 2026Article
- Embodied decisions as active inference.PLoS computational biology · 2025Article
- Deep Hybrid Models: Infer and Plan in a Dynamic World.Entropy (Basel, Switzerland) · 2025Article
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- Article
- Deep kinematic inference affords efficient and scalable control of bodily movements.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Active Vision in Binocular Depth Estimation: A Top-Down Perspective.Biomimetics (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
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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. 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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Registered trials
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