标题： 从非结构化动物运动中学习可调节的模仿控制器 显示英文标题

标题： Learning Steerable Imitation Controllers from Unstructured Animal Motions

摘要： 本文提出了一种用于腿式机器人的控制框架，该框架利用非结构化的现实世界动物运动数据生成类似动物且可由用户控制的行为。 我们的框架学习在遵循速度指令的同时，再现原始数据集中的多种步态模式。 首先，使用约束逆运动学和模型预测控制将动物运动数据转换为机器人兼容的数据库，弥合了动物与机器人之间的形态和物理差距。 随后，基于变分自编码器的运动合成模块捕捉运动数据库中的多种运动模式，并根据速度指令生成平滑的过渡。 生成的运动学动作作为强化学习反馈控制器的参考，该控制器部署在实体机器人上。 我们展示了这种方法使四足机器人能够适应性地切换步态，并准确跟踪用户速度指令，同时保持运动数据的风格一致性。 此外，我们提供了逐组件评估，以深入分析系统行为，并证明了我们方法在更精确和可靠的运动模仿中的有效性。 显示英文摘要

摘要： This paper presents a control framework for legged robots that leverages unstructured real-world animal motion data to generate animal-like and user-steerable behaviors. Our framework learns to follow velocity commands while reproducing the diverse gait patterns in the original dataset. To begin with, animal motion data is transformed into a robot-compatible database using constrained inverse kinematics and model predictive control, bridging the morphological and physical gap between the animal and the robot. Subsequently, a variational autoencoder-based motion synthesis module captures the diverse locomotion patterns in the motion database and generates smooth transitions between them in response to velocity commands. The resulting kinematic motions serve as references for a reinforcement learning-based feedback controller deployed on physical robots. We show that this approach enables a quadruped robot to adaptively switch gaits and accurately track user velocity commands while maintaining the stylistic coherence of the motion data. Additionally, we provide component-wise evaluations to analyze the system's behavior in depth and demonstrate the efficacy of our method for more accurate and reliable motion imitation.