  Locomotion has seen dramatic progress for walking or running across
challenging terrains. However, robotic quadrupeds are still far behind their
biological counterparts, such as dogs, which display a variety of agile skills
and can use the legs beyond locomotion to perform several basic manipulation
tasks like interacting with objects and climbing. In this paper, we take a step
towards bridging this gap by training quadruped robots not only to walk but
also to use the front legs to climb walls, press buttons, and perform object
interaction in the real world. To handle this challenging optimization, we
decouple the skill learning broadly into locomotion, which involves anything
that involves movement whether via walking or climbing a wall, and
manipulation, which involves using one leg to interact while balancing on the
other three legs. These skills are trained in simulation using curriculum and
transferred to the real world using our proposed sim2real variant that builds
upon recent locomotion success. Finally, we combine these skills into a robust
long-term plan by learning a behavior tree that encodes a high-level task
hierarchy from one clean expert demonstration. We evaluate our method in both
simulation and real-world showing successful executions of both short as well
as long-range tasks and how robustness helps confront external perturbations.
Videos at https://robot-skills.github.io
