Editorial: Robots that Learn and Reason: Towards Learning Logic Rules from Noisy Data

From the early developments of AI applied to robotics by Hart et al. (1968), Duda and Hart (1972) and Lozano-Pérez and Wesley (1979), higher level commands were grounded to real world sensing by carefully design algorithms, which provide a link between the abstract predicates and the sensors and actuators. In order to have fully autonomous robots that learn by exploration and by imitation, the grounding algorithms between the higher-level predicates and the lower-level sensors and actuators should be discovered by the robot. Previous and recent efforts on robotics aim to discover and/or learn these intermediate layer commands, which must cope with discrete and continuous data. The main objective of this Research Topic is to advance on learning logic rules from noisy data. We have four articles that address: Logic rules that cope with states that are not directly observable by the sensing modalities; learning rules that represent object properties and their functionalities, which are grounded to the particular robot experience; learning low-level robot control actions that fulfill a set of abstract predicates in a two-level planning approach; learning to develop skills in a robotic playing scenario by composing a set of behaviors. In the following, we introduce the four articles and their contributions to rule learning in presence of noisy data.