  Mobile manipulation in robotics is challenging due to the need of solving
many diverse tasks, such as opening a door or picking-and-placing an object.
Typically, a basic first-principles system description of the robot is
available, thus motivating the use of model-based controllers. However, the
robot dynamics and its interaction with an object are affected by uncertainty,
limiting the controller's performance. To tackle this problem, we propose a
Bayesian multi-task learning model that uses trigonometric basis functions to
identify the error in the dynamics. In this way, data from different but
related tasks can be leveraged to provide a descriptive error model that can be
efficiently updated online for new, unseen tasks. We combine this learning
scheme with a model predictive controller, and extensively test the
effectiveness of the proposed approach, including comparisons with available
baseline controllers. We present simulation tests with a ball-balancing robot,
and door-opening hardware experiments with a quadrupedal manipulator.
