{"ID":2886898,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.06538","arxiv_id":"2508.06538","title":"Symbolic Learning of Interpretable Reduced-Order Models for Jumping Quadruped Robots","abstract":"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.","short_abstract":"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 methodo...","url_abs":"https://arxiv.org/abs/2508.06538","url_pdf":"https://arxiv.org/pdf/2508.06538v2","authors":"[\"Gioele Buriani\",\"Jingyue Liu\",\"Maximilian Stölzle\",\"Cosimo Della Santina\",\"Jiatao Ding\"]","published":"2025-08-04T12:33:51Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"eess.SY\"]","methods":"[]","has_code":false}