# Learning Task-Agnostic Motifs to Capture the Continuous Nature of Animal Behavior

arXiv: https://arxiv.org/abs/2506.15190 · HTML: https://mlsanity.com/paper/2506.15190/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 59 references that are in the map.
- First posted on arXiv 18 Jun 2025.

## Builds on (most cited first, 27 of 27)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Diversity is All You Need: Learning Skills without a Reward Function](https://mlsanity.com/paper/1802.06070/) (2018)
- [Dynamics-Aware Unsupervised Discovery of Skills](https://mlsanity.com/paper/1907.01657/) (2019)
- [Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss](https://mlsanity.com/paper/2106.04156/) (2021)
- [Accelerating Reinforcement Learning with Learned Skill Priors](https://mlsanity.com/paper/2010.11944/) (2020)
- [IQ-Learn: Inverse soft-Q Learning for Imitation](https://mlsanity.com/paper/2106.12142/) (2021)
- [Imitation Learning via Off-Policy Distribution Matching](https://mlsanity.com/paper/1912.05032/) (2019)
- [OPAL: Offline Primitive Discovery for Accelerating Offline Reinforcement Learning](https://mlsanity.com/paper/2010.13611/) (2020)
- [Parrot: Data-Driven Behavioral Priors for Reinforcement Learning](https://mlsanity.com/paper/2011.10024/) (2020)
- [Learning One Representation to Optimize All Rewards](https://mlsanity.com/paper/2103.07945/) (2021)
- [Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency](https://mlsanity.com/paper/1809.01812/) (2018)
- [Universal Successor Features Approximators](https://mlsanity.com/paper/1812.07626/) (2018)
- [Foundation Policies with Hilbert Representations](https://mlsanity.com/paper/2402.15567/) (2024)
- [Demonstration-Guided Reinforcement Learning with Learned Skills](https://mlsanity.com/paper/2107.10253/) (2021)
- [Making Linear MDPs Practical via Contrastive Representation Learning](https://mlsanity.com/paper/2207.07150/) (2022)
- [Dynamic Movement Primitives in Robotics: A Tutorial Survey](https://mlsanity.com/paper/2102.03861/) (2021)
- [Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information](https://mlsanity.com/paper/1810.01266/) (2018)
- [TRAIL: Near-Optimal Imitation Learning with Suboptimal Data](https://mlsanity.com/paper/2110.14770/) (2021)
- [Spectral Decomposition Representation for Reinforcement Learning](https://mlsanity.com/paper/2208.09515/) (2022)
- [Behavior Contrastive Learning for Unsupervised Skill Discovery](https://mlsanity.com/paper/2305.04477/) (2023)
- [Mapping the stereotyped behaviour of freely-moving fruit flies](https://mlsanity.com/paper/1310.4249/) (2013)
- [Diffusion Spectral Representation for Reinforcement Learning](https://mlsanity.com/paper/2406.16121/) (2024)
- [Self-Supervised Reinforcement Learning that Transfers using Random Features](https://mlsanity.com/paper/2305.17250/) (2023)
- [Multi-intention Inverse Q-learning for Interpretable Behavior Representation](https://mlsanity.com/paper/2311.13870/) (2023)
- [SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending](https://mlsanity.com/paper/2506.09366/) (2025)
- [Inverse Reinforcement Learning with Switching Rewards and History Dependency for Characterizing Animal Behaviors](https://mlsanity.com/paper/2501.12633/) (2025)
- [Noise Contrastive Estimation for Scalable Linear Models for One-Class Collaborative Filtering](https://mlsanity.com/paper/1811.00697/) (2018)

## Built on by (most cited first, 1 of 1)

- [Fitting Reinforcement Learning Model to Behavioral Data under Bandits](https://mlsanity.com/paper/2511.04454/) (2025)

## Abstract (arXiv)

Animals flexibly recombine a finite set of core motor motifs to meet diverse task demands, but existing behavior segmentation methods oversimplify this process by imposing discrete syllables under restrictive generative assumptions. To better capture the continuous structure of behavior generation, we introduce motif-based continuous dynamics (MCD) discovery, a framework that (1) uncovers interpretable motif sets as latent basis functions of behavior by leveraging representations of behavioral transition structure, and (2) models behavioral dynamics as continuously evolving mixtures of these motifs. We validate MCD on a multi-task gridworld, a labyrinth navigation task, and freely moving animal behavior. Across settings, it identifies reusable motif components, captures continuous compositional dynamics, and generates realistic trajectories beyond the capabilities of traditional discrete segmentation models. By providing a generative account of how complex animal behaviors emerge from dynamic combinations of fundamental motor motifs, our approach advances the quantitative study of natural behavior.