为四足机器人Go2构建800条多样步态数据集，助力机器学习控制研究
Kine2Go: Kinematic dataset for the Unitree Go2 robot with diverse gaits and motions
- 从40种策略生成运动轨迹，经强化学习训练后采集鲁棒数据
- 包含800条带电机动作的关节运动数据，支持多种步态与扰动场景
- 适配科研人员做模仿学习、强化学习等机器人控制实验
随着机器人普及和硬件成本下降，四足机器人研究迅速发展。其中，Unitree Go2因其开放性常被用于步态、导航与控制研究。许多基于模仿学习、强化学习和行为克隆的方法需要高质量的机器人运动学数据与电机动作记录，但获取此类数据需复杂流程且耗时。为此，我们构建了Kine2Go数据集，包含800条来自40种不同策略的多样化步态运动轨迹数据，涵盖关节运动学信息与对应电机指令。我们的管道可兼容多种四足形态，将其转换为Go2适配格式；通过强化学习训练策略以跟踪目标运动，再从中采集具有鲁棒性与扰动特性的运动数据，有效支持机器学习驱动的机器人控制研究。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。