  Autonomous mobile manipulation offers a dual advantage of mobility provided
by a mobile platform and dexterity afforded by the manipulator. In this paper,
we present a whole-body optimal control framework to jointly solve the problems
of manipulation, balancing and interaction as one optimization problem for an
inherently unstable robot. The optimization is performed using a Model
Predictive Control (MPC) approach; the optimal control problem is transcribed
at the end-effector space, treating the position and orientation tasks in the
MPC planner, and skillfully planning for end-effector contact forces. The
proposed formulation evaluates how the control decisions aimed at end-effector
tracking and environment interaction will affect the balance of the system in
the future. We showcase the advantages of the proposed MPC approach on the
example of a ball-balancing robot with a robotic manipulator and validate our
controller in hardware experiments for tasks such as end-effector pose tracking
and door opening.
