Integrated motor control, planning, grasping and high-level reasoning in a blocks world using probabilistic inference
Marc Toussaint, Nils Plath, Tobias Lang and Nikolay Jetchev
In: ICRA 2010(2010).
Abstract—A new approach to planning and goal-directed behavior
has recently been proposed using probabilistic inference
in a graphical model that represents states, actions, constraints
and goals of the future to infer appropriate actions and controls.
The approach has led to new algorithms on the control and
trajectory optimization level as well as for high-level rule-based
planning in relational domains. In this paper we integrate these
methods to a coherent control, trajectory optimization, and
action planning architecture, using the principle of planning
by inference across all levels of abstractions. Our scenario is a
real blocks world: using a 14DoF Schunk arm and hand with
tactile sensors and a stereo camera, the goal is to manipulate a
set of objects on the table in a goal-oriented way. For highlevel
reasoning, we learn relational rule-based models from
experience in simulation.