Robotic manipulation in semi-structured and changing environments requires systems with: a) perception and reasoning capabilities able to capture and understand the state of the environment; b) planning and replanning capabilities at both symbolic and geometric levels; c) automatic and robust execution capabilities. To cope with these issues, this paper presents a framework with the following features. First, it uses perception and ontology-based reasoning procedures to obtain the Planning Description Domain Language files that describe the manipulation problem at task level. This is used in the planning stage as well as during task execution in order to adapt to new situations, if required. Second, the proposed framework is able to plan at both task and motion levels, intertwining them by incorporating geometric reasoning modules to determine some of the symbolic predicates needed to describe the states. Finally, the framework automatically generates the behavior trees required to execute the task. The proposal takes advantage of the ability of behavior trees to be edited during run time, allowing adaptation of the action plan or of the trajectories according to changes in the state of the environment. The approach allows for robot manipulation tasks to be automatically planned and robustly executed, contributing to achieve fully functional service robots.

Robots should be able to exercise reasoning in both symbolic and geometric levels in order to plan a manipulation task. The execution of such tasks needs to be robust enough to cope with real environments. In an attempt to address this pertinent industry need, the paper proposes the use of behavior trees for effective robotic manipulation in dynamic environments. This paper presents a method to automatically generate a behavior tree and showcases its ability to enable the robot to reason at different levels and adapt to an uncertain and changing environment. This allows for a complex task to be robustly executed, pioneering the advancement towards fully functional service robots.