  Quadruped robots have shown remarkable mobility on various terrains through
reinforcement learning. Yet, in the presence of sparse footholds and risky
terrains such as stepping stones and balance beams, which require precise foot
placement to avoid falls, model-based approaches are often used. In this paper,
we show that end-to-end reinforcement learning can also enable the robot to
traverse risky terrains with dynamic motions. To this end, our approach
involves training a generalist policy for agile locomotion on disorderly and
sparse stepping stones before transferring its reusable knowledge to various
more challenging terrains by finetuning specialist policies from it. Given that
the robot needs to rapidly adapt its velocity on these terrains, we formulate
the task as a navigation task instead of the commonly used velocity tracking
which constrains the robot's behavior and propose an exploration strategy to
overcome sparse rewards and achieve high robustness. We validate our proposed
method through simulation and real-world experiments on an ANYmal-D robot
achieving peak forward velocity of >= 2.5 m/s on sparse stepping stones and
narrow balance beams. Video: youtu.be/Z5X0J8OH6z4
