  In recent years, legged and wheeled-legged robots have gained prominence for
tasks in environments predominantly created for humans across various domains.
One significant challenge faced by many of these robots is their limited
capability to navigate stairs, which hampers their functionality in multi-story
environments. This study proposes a method aimed at addressing this limitation,
employing reinforcement learning to develop a versatile controller applicable
to a wide range of robots. In contrast to the conventional velocity-based
controllers, our approach builds upon a position-based formulation of the RL
task, which we show to be vital for stair climbing. Furthermore, the
methodology leverages an asymmetric actor-critic structure, enabling the
utilization of privileged information from simulated environments during
training while eliminating the reliance on exteroceptive sensors during
real-world deployment. Another key feature of the proposed approach is the
incorporation of a boolean observation within the controller, enabling the
activation or deactivation of a stair-climbing mode. We present our results on
different quadrupeds and bipedal robots in simulation and showcase how our
method allows the balancing robot Ascento to climb 15cm stairs in the real
world, a task that was previously impossible for this robot.
