Agile Continuous Jumping in Discontinuous Terrains
Authors: name: Yuxiang Yang, name: Guanya Shi, name: Changyi Lin, name: Xiangyun Meng, name: Rosario Scalise, name: Mateo Guaman Castro, name: Wenhao Yu, name: Tingnan Zhang, name: Ding Zhao, name: Jie Tan, name: Byron Boots

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/.