Integrated motor control, planning, grasping and high-level reasoning in a blocks world using probabilistic inference

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 high-level reasoning, we learn relational rule-based models from experience in simulation.