  Learning from demonstrations is a promising paradigm for transferring
knowledge to robots. However, learning mobile manipulation tasks directly from
a human teacher is a complex problem as it requires learning models of both the
overall task goal and of the underlying actions. Additionally, learning from a
small number of demonstrations often introduces ambiguity with respect to the
intention of the teacher, making it challenging to commit to one model for
generalizing the task to new settings. In this paper, we present an approach to
learning flexible mobile manipulation action models and task goal
representations from teacher demonstrations. Our action models enable the robot
to consider different likely outcomes of each action and to generate feasible
trajectories for achieving them. Accordingly, we leverage a probabilistic
framework based on Monte Carlo tree search to compute sequences of feasible
actions imitating the teacher intention in new settings without requiring the
teacher to specify an explicit goal state. We demonstrate the effectiveness of
our approach in complex tasks carried out in real-world settings.
