  Modern, torque-controlled service robots can regulate contact forces when
interacting with their environment. Model Predictive Control (MPC) is a
powerful method to solve the underlying control problem, allowing to plan for
whole-body motions while including different constraints imposed by the robot
dynamics or its environment. However, an accurate model of the
robot-environment is needed to achieve a satisfying closed-loop performance.
Currently, this necessity undermines the performance and generality of MPC in
manipulation tasks. In this work, we combine an MPC-based whole-body controller
with two adaptive schemes, derived from online system identification and
adaptive control. As a result, we enable a general mobile manipulator to
interact with unknown environments, without any need for re-tuning parameters
or pre-modeling the interacting objects. In combination with the MPC
controller, the two adaptive approaches are validated and benchmarked with a
ball-balancing manipulator in door opening and object lifting tasks.
