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Learning Reusable Motor Motifs for Continuous Animal Behavior Modeling

NeurIPS 2026 · Reinforcement learning › Visual and representation RL · approach: Learned state and action representations

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AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Proposes Motif-based Continuous Dynamics (MCD), a framework that models animal behavior as a continuous, compositional process built from reusable motor motifs, as an alternative to discrete syllable segmentation.

Problem

Existing animal behavior segmentation methods oversimplify behavior by imposing discrete syllables under restrictive generative assumptions, failing to capture flexible recombination of motor primitives.

Approach

Models behavior as continuous dynamics composed from a finite set of reusable motor motifs, rather than discrete syllables; the abstract mentions leveraging reinforcement learning but the remainder of the method is not stated in the available text.

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