不连续地形中的敏捷连续跳跃

Agile Continuous Jumping in Discontinuous Terrains

September 17, 2024

作者: Yuxiang Yang, Guanya Shi, Changyi Lin, Xiangyun Meng, Rosario Scalise, Mateo Guaman Castro, Wenhao Yu, Tingnan Zhang, Ding Zhao, Jie Tan, Byron Boots

cs.AI

摘要

我们专注于四足机器人在楼梯和踏石等不连续地形中的敏捷、连续和适应性跳跃。与单步跳跃不同，连续跳跃需要准确执行长时间跨度内的高动态运动，这对现有方法来说是具有挑战性的。为了完成这一任务，我们设计了一个分层学习和控制框架，包括用于稳健地形感知的学习高度图预测器、基于强化学习的质心级运动策略以实现多功能和地形自适应规划，以及用于精确运动跟踪的基于模型的低级腿部控制器。此外，我们通过准确建模硬件特性来最小化仿真到实际的差距。我们的框架使得Unitree Go1机器人能够据我们所知首次在人高的楼梯和稀疏的踏石上执行敏捷和连续的跳跃。特别地，该机器人可以在每次跳跃中跨越两个楼梯台阶，并在4.5秒内完成一段长3.5米、高2.8米、14级台阶的楼梯。此外，相同策略在各种其他跑酷任务中表现优异，如跳过单个水平或垂直不连续。实验视频可在https://yxyang.github.io/jumping\_cod/找到。

English

We focus on agile, continuous, and terrain-adaptive jumping of quadrupedal robots in discontinuous terrains such as stairs and stepping stones. Unlike single-step jumping, continuous jumping requires accurately executing highly dynamic motions over long horizons, which is challenging for existing approaches. To accomplish this task, we design a hierarchical learning and control framework, which consists of a learned heightmap predictor for robust terrain perception, a reinforcement-learning-based centroidal-level motion policy for versatile and terrain-adaptive planning, and a low-level model-based leg controller for accurate motion tracking. In addition, we minimize the sim-to-real gap by accurately modeling the hardware characteristics. Our framework enables a Unitree Go1 robot to perform agile and continuous jumps on human-sized stairs and sparse stepping stones, for the first time to the best of our knowledge. In particular, the robot can cross two stair steps in each jump and completes a 3.5m long, 2.8m high, 14-step staircase in 4.5 seconds. Moreover, the same policy outperforms baselines in various other parkour tasks, such as jumping over single horizontal or vertical discontinuities. Experiment videos can be found at https://yxyang.github.io/jumping\_cod/.