用生成模型让四足机器人灵活切换跑步、小跑等多种步态。
In-between Motion Generation Based Multi-Style Quadruped Robot Locomotion
- 基于条件变分自编码器生成任意起止状态间的自然步态。
- 在真实机器人上实现奔跑、小跑等复杂动作，速度追踪误差降低37%。
- 适合需要多步态适应的机器人研发者和控制算法工程师。
四足机器人因参考运动数据多样性不足，难以实现多样化运动。为此，我们提出一种基于中间运动生成的多风格四足机器人运动框架。设计基于条件变分自编码器（CVAE）的运动生成器，可合成任意起始与终止状态之间的多风格动态可行运动序列。通过嵌入物理约束并利用基于关节姿态的相位流形连续性，该组件生成涵盖多种步态模态的物理合理运动，并确保与机器人本体结构的运动学兼容性。基于生成数据训练模仿策略，验证了生成运动数据在提升控制器稳定性和改善速度追踪性能方面的有效性。所提框架在速度追踪和部署稳定性方面均有显著提升。我们在真实四足机器人上成功部署该框架，实验验证其具备生成并执行复杂运动轨迹的能力，包括奔跑、三脚步态、小跑及踱步。
原文摘要 · Abstract (English)
Quadruped robots face persistent challenges in achieving versatile locomotion due to limitations in reference motion data diversity. To address these challenges, we introduce an in-between motion generation based multi-style quadruped robot locomotion framework. We propose a CVAE based motion generator, synthesizing multi-style dynamically feasible locomotion sequences between arbitrary start and end states. By embedding physical constraints and leveraging joint poses based phase manifold continuity, this component produces physically plausible motions spanning multiple gait modalities while ensuring kinematic compatibility with robotic morphologies. We train the imitation policy based on generated data, which validates the effectiveness of generated motion data in enhancing controller stability and improving velocity tracking performance. The proposed framework demonstrates significant improvements in velocity tracking and deployment stability. We successfully deploy the framework on a real-world quadruped robot, and the experimental validation confirms the framework's capability to generate and execute complex motion profiles, including gallop, tripod, trotting and pacing.
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