The recent popularity of robotics, combined with the steadily decreasing cost of robotic hardware, has lowered the entry barrier to robotics research and enabled rapid advancements in the field. One of the primary examples is the Unitree Go2 quadruped robot, which is often used by researchers in the areas of locomotion, navigation, control, and others. Many researchers use the Go2 robot in combination with techniques like imitation learning, reinforcement learning, and behavioral cloning to allow machine learning systems to take full control of the robot. At the same time, many of those techniques require demonstration data consisting of the robot's kinematics information and actions applied to the motors. Obtaining such data is difficult, requires building complex pipelines, and can take significant time. To aid in those kinds of efforts, we present Kine2Go - a dataset with 800 diverse gait kinematics trajectory motion data for the Unitree Go2 robot, derived from 40 distinct policies. Our pipeline accepts data from various quadruped morphologies and translates them to a Go2-compatible format. Then we use Reinforcement Learning to train policies following a given motion, and finally we gather data from those policies, which grants robust, perturbed kinematic data with corresponding motor-level actions.

翻译：近年来，机器人技术的普及以及硬件成本的持续下降，降低了机器人研究的准入门槛，推动了该领域的快速发展。Unitree Go2四足机器人便是典型实例，被广泛应用于运动控制、导航、控制算法等研究方向。许多研究者将Go2与模仿学习、强化学习、行为克隆等技术相结合，使机器学习系统能够完全自主控制机器人。然而，这些技术通常需要包含机器人运动学信息和电机控制动作的演示数据。获取此类数据难度较大，需构建复杂的处理流程且耗时显著。为助力相关研究，我们提出Kine2Go数据集——包含基于40种不同策略生成的800条涵盖多种步态的运动学轨迹数据。我们的处理流程可接收不同四足形态的输入数据，并将其转换为Go2兼容格式，随后通过强化学习训练策略以遵循指定运动模式，最终从这些策略中采集数据，获得包含对应电机级动作的鲁棒扰动运动学数据。