基于中间运动生成的多风格四足机器人运动控制
In-between Motion Generation Based Multi-Style Quadruped Robot Locomotion
AI总结：
针对四足机器人参考运动数据多样性受限的问题，提出基于中间运动生成的多风格运动框架，利用CVAE生成器合成满足物理约束的多步态运动序列，并基于生成数据训练模仿策略，在真实机器人上验证了速度跟踪与部署稳定性的显著提升。
摘要：
由于参考运动数据多样性的限制，四足机器人在实现多功能运动方面面临持续挑战。为解决这些问题，我们提出了一种基于中间运动生成的多风格四足机器人运动框架。我们设计了一个基于CVAE的运动生成器，用于在任意起始和结束状态之间合成多风格且动态可行的运动序列。通过嵌入物理约束并利用基于关节位姿的相流形连续性，该组件能生成跨越多种步态模态且物理合理的运动，同时确保与机器人形态的运动学兼容性。我们基于生成数据训练模仿策略，验证了生成运动数据在增强控制器稳定性和提升速度跟踪性能方面的有效性。所提框架在速度跟踪和部署稳定性上展现出显著提升。我们成功将该框架部署于真实四足机器人上，实验验证确认了其生成并执行复杂运动模式的能力，包括gallop、tripod、trotting和pacing。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
