OmniMimic: Dynamics-completed Motion Augmentation for Multi-style Omnidirectional Quadruped Locomotion

2026-09-17

AI Summary

OmniMimic通过扩展动物演示数据的方向覆盖范围，并利用动态完成和专门的残差专家来训练四足机器人，以实现全方向多风格运动。

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.

Problem

• quadruped robots
• animal demonstrations
• directional coverage
• gait styles
• locomotion

Innovation

• Dynamics-completed Motion Augmentation
• Multi-style Omnidirectional Quadruped Locomotion
• Temporal Reversal
• Constrained Dynamics Completion
• Sagittal Reflection