Kine2Go: Kinematic dataset for the Unitree Go2 robot with diverse gaits and motions

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 state-action locomotion dataset for the Unitree Go2 robot comprising 40 motion families, each associated with an RL tracking policy and represented by 20 stochastic rollouts. 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 yields perturbed kinematic trajectories with corresponding motor-level actions. To showcase the value of our dataset, we also train a Behavioral Foundation Model (BFM) on it and evaluate its motion-tracking and zero-shot reward-conditioning capabilities.