Agile Continuous Jumping in Discontinuous Terrains

We focus on advancing the agility of quadrupedal robots with continuous, precise, and terrain-adaptive jumping in discontinuous terrains such as stairs and stepping stones.

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.

Such a hierarchical and hybrid framework effectively combines the advantages of model-free learning and model-based control, therefore enabling 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 stair in 4.5 seconds. Moreover, the same policy outperforms baselines in various other parkour tasks, such as jumping over single horizontal or vertical discontinuities.