{"ID":23507345,"CreatedAt":"2026-09-18T02:21:44.056544415Z","UpdatedAt":"2026-09-20T18:11:56.143995915Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2609.20566","arxiv_id":"2609.20566","title":"OmniMimic: Dynamics-completed Motion Augmentation for Multi-style Omnidirectional Quadruped Locomotion","abstract":"Animal demonstrations provide quadruped robots with natural and distinctive gait styles that are difficult to specify through hand-crafted rewards. However, their narrow directional coverage leaves little style-consistent supervision for backward, lateral, and turning commands. We present OmniMimic, a training framework that turns directionally limited animal demonstrations into a single multi-gait policy over target per-axis velocity ranges. OmniMimic first combines temporal reversal, constrained dynamics completion, and sagittal reflection to construct robot-specific kinematic and physical supervision beyond the observed directions. It then expands commands progressively from the demonstrated velocity distribution toward the target per-axis bounds, and uses a shared actor with soft-gated, gait-specialized residual experts to balance reusable locomotion skills with gait-specific corrections. Across four gaits in simulation, OmniMimic reduces mean foot-position RMSE at forward and backward reference velocities by 12.9% and velocity-tracking RMSE on a uniform Cartesian command grid by 63.1%, compared with the matched APEX baseline. The project page is at https://OmniMimic.github.io.","short_abstract":"Animal demonstrations provide quadruped robots with natural and distinctive gait styles that are difficult to specify through hand-crafted rewards. However, their narrow directional coverage leaves little style-consistent supervision for backward, lateral, and turning commands. We present OmniMimic, a training framewor...","url_abs":"https://arxiv.org/abs/2609.20566","url_pdf":"https://arxiv.org/pdf/2609.20566v1","authors":"[\"Sheng Wu\",\"Guoqiang Zhao\",\"Zhe Yang\",\"Fei Teng\",\"Zhikun Zhou\",\"Yanlin Yang\",\"Zheng Fang\",\"Hong Zheng\",\"Yaonan Wang\",\"Kailun Yang\"]","published":"2026-09-17T15:28:48Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\",\"eess.IV\"]","methods":"[]","has_code":false}