不連續地形中的敏捷連續跳躍

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

我們專注於四足機器人在不連續地形（如樓梯和踏石）上的敏捷、連續和適應性跳躍。與單步跳躍不同，連續跳躍需要準確執行高度動態運動，並在長時間範圍內進行，這對現有方法來說是具有挑戰性的。為了完成這項任務，我們設計了一個分層學習和控制框架，其中包括用於穩健地形感知的學習高度圖預測器、基於強化學習的重心級運動策略以進行多功能和適應性規劃，以及用於準確運動跟踪的基於模型的低級腿部控制器。此外，我們通過準確建模硬件特性來最小化模擬與現實之間的差距。據我們所知，我們的框架使得 Unitree Go1 機器人能夠在人身高的樓梯和稀疏踏石上首次執行敏捷和連續跳躍。特別是，該機器人可以在每次跳躍中跨越兩個樓梯階，並在 4.5 秒內完成長 3.5 米、高 2.8 米、14 級樓梯。此外，相同策略在各種其他跑酷任務中優於基準線，例如跳過單個水平或垂直不連續處。實驗視頻可在 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/.