  An important prerequisite for the reliability and robustness of a service
robot is ensuring the robot's correct behavior when it performs various tasks
of interest. Extensive testing is one established approach for ensuring
behavioural correctness; this becomes even more important with the integration
of learning-based methods into robot software architectures, as there are often
no theoretical guarantees about the performance of such methods in varying
scenarios. In this paper, we aim towards evaluating the correctness of robot
behaviors in tabletop manipulation through automatic generation of simulated
test scenarios in which a robot assesses its performance using property-based
testing. In particular, key properties of interest for various robot actions
are encoded in an action ontology and are then verified and validated within a
simulated environment. We evaluate our framework with a Toyota Human Support
Robot (HSR) which is tested in a Gazebo simulation. We show that our framework
can correctly and consistently identify various failed actions in a variety of
randomised tabletop manipulation scenarios, in addition to providing deeper
insights into the type and location of failures for each designed property.
