Reasoning and state monitoring for the robust execution of robotic manipulation tasks

Abstract

The execution of robotic manipulation tasks needs to be robust in front of failures or changes in the environment, and for this purpose, Behavior Trees (BT) are a good alternative to Finite State Machines, because the ability of BTs to be edited during run time and the fact that one can design reactive systems with BTs, makes the BT executor a robust execution manager. However, the good monitoring of the system state is required in order to react to errors at either geometric or symbolic level requiring, respectively, replanning at motion or at task level. This paper make a proposal in this line and, moreover, makes task planning adaptive to the actual situations encountered by knowledge-based reasoning procedures to automatically generate the Planning Domain Definition Language (PDDL) files that define the task.