Symbolic Learning of Interpretable Reduced-Order Models for Jumping Quadruped Robots
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909988155293696 |
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| author | Buriani, Gioele Liu, Jingyue Stölzle, Maximilian Della Santina, Cosimo Ding, Jiatao |
| author_facet | Buriani, Gioele Liu, Jingyue Stölzle, Maximilian Della Santina, Cosimo Ding, Jiatao |
| contents | Reduced-order models are central to motion planning and control of quadruped robots, yet existing templates are often hand-crafted for a specific locomotion modality. This motivates the need for automatic methods that extract task-specific, interpretable low-dimensional dynamics directly from data. We propose a methodology that combines a linear autoencoder with symbolic regression to derive such models. The linear autoencoder provides a consistent latent embedding for configurations, velocities, accelerations, and inputs, enabling the sparse identification of nonlinear dynamics (SINDy) to operate in a compact, physics-aligned space. A multi-phase, hybrid-aware training scheme ensures coherent latent coordinates across contact transitions. We focus our validation on quadruped jumping-a representative, challenging, yet contained scenario in which a principled template model is especially valuable. The resulting symbolic dynamics outperform the state-of-the-art handcrafted actuated spring-loaded inverted pendulum (aSLIP) baseline in simulation and hardware across multiple robots and jumping modalities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06538 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Symbolic Learning of Interpretable Reduced-Order Models for Jumping Quadruped Robots Buriani, Gioele Liu, Jingyue Stölzle, Maximilian Della Santina, Cosimo Ding, Jiatao Robotics Artificial Intelligence Systems and Control Reduced-order models are central to motion planning and control of quadruped robots, yet existing templates are often hand-crafted for a specific locomotion modality. This motivates the need for automatic methods that extract task-specific, interpretable low-dimensional dynamics directly from data. We propose a methodology that combines a linear autoencoder with symbolic regression to derive such models. The linear autoencoder provides a consistent latent embedding for configurations, velocities, accelerations, and inputs, enabling the sparse identification of nonlinear dynamics (SINDy) to operate in a compact, physics-aligned space. A multi-phase, hybrid-aware training scheme ensures coherent latent coordinates across contact transitions. We focus our validation on quadruped jumping-a representative, challenging, yet contained scenario in which a principled template model is especially valuable. The resulting symbolic dynamics outperform the state-of-the-art handcrafted actuated spring-loaded inverted pendulum (aSLIP) baseline in simulation and hardware across multiple robots and jumping modalities. |
| title | Symbolic Learning of Interpretable Reduced-Order Models for Jumping Quadruped Robots |
| topic | Robotics Artificial Intelligence Systems and Control |
| url | https://arxiv.org/abs/2508.06538 |