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The software of robotic assistants needs to be verified, to ensure its safetyand functional correctness. Testing in simulation allows a high degree ofrealism in the verification. However, generating tests that cover bothinteresting foreseen and unforeseen scenarios in human-robot interaction (HRI)tasks, while executing most of the code, remains a challenge. We propose theuse of belief-desire-intention (BDI) agents in the test environment, toincrease the level of realism and human-like stimulation of simulated robots.Artificial intelligence, such as agent theory, can be exploited for moreintelligent test generation. An automated testbench was implemented for asimulation in Robot Operating System (ROS) and Gazebo, of a cooperative tableassembly task between a humanoid robot and a person. Requirements were verifiedfor this task, and some unexpected design issues were discovered, leading topossible code improvements. Our results highlight the practicality of BDIagents to automatically generate valid and human-like tests to get high codecoverage, compared to hand-written directed tests, pseudorandom generation, andother variants of model-based test generation. Also, BDI agents allow thecoverage of combined behaviours of the HRI system with more ease than writingtemporal logic properties for model checking.

Model-Based Testing, Using Belief-Desire-Intentions Agents, of Control Code for Robots in Collaborative Human-Robot Interactions