  An attached arm can significantly increase the applicability of legged robots
to several mobile manipulation tasks that are not possible for the wheeled or
tracked counterparts. The standard hierarchical control pipeline for such
legged manipulators is to decouple the controller into that of manipulation and
locomotion. However, this is ineffective. It requires immense engineering to
support coordination between the arm and legs, and error can propagate across
modules causing non-smooth unnatural motions. It is also biological implausible
given evidence for strong motor synergies across limbs. In this work, we
propose to learn a unified policy for whole-body control of a legged
manipulator using reinforcement learning. We propose Regularized Online
Adaptation to bridge the Sim2Real gap for high-DoF control, and Advantage
Mixing exploiting the causal dependency in the action space to overcome local
minima during training the whole-body system. We also present a simple design
for a low-cost legged manipulator, and find that our unified policy can
demonstrate dynamic and agile behaviors across several task setups. Videos are
at https://maniploco.github.io
