  Legged robots have the potential to become vital in maintenance, home
support, and exploration scenarios. In order to interact with and manipulate
their environments, most legged robots are equipped with a dedicated robot arm,
which means additional mass and mechanical complexity compared to standard
legged robots. In this work, we explore pedipulation - using the legs of a
legged robot for manipulation. By training a reinforcement learning policy that
tracks position targets for one foot, we enable a dedicated pedipulation
controller that is robust to disturbances, has a large workspace through
whole-body behaviors, and can reach far-away targets with gait emergence,
enabling loco-pedipulation. By deploying our controller on a quadrupedal robot
using teleoperation, we demonstrate various real-world tasks such as door
opening, sample collection, and pushing obstacles. We demonstrate load carrying
of more than 2.0 kg at the foot. Additionally, the controller is robust to
interaction forces at the foot, disturbances at the base, and slippery contact
surfaces. Videos of the experiments are available at
https://sites.google.com/leggedrobotics.com/pedipulate.
