  We study the problem of mobile manipulation using legged robots equipped with
an arm, namely legged loco-manipulation. The robot legs, while usually utilized
for mobility, offer an opportunity to amplify the manipulation capabilities by
conducting whole-body control. That is, the robot can control the legs and the
arm at the same time to extend its workspace. We propose a framework that can
conduct the whole-body control autonomously with visual observations. Our
approach, namely Visual Whole-Body Control(VBC), is composed of a low-level
policy using all degrees of freedom to track the body velocities along with the
end-effector position, and a high-level policy proposing the velocities and
end-effector position based on visual inputs. We train both levels of policies
in simulation and perform Sim2Real transfer for real robot deployment. We
perform extensive experiments and show significant improvements over baselines
in picking up diverse objects in different configurations (heights, locations,
orientations) and environments.
