Intelligent Agent-Based Stimulation for Testing Robotic Software in Human-Robot Interactions

Dejanira Araiza Illan, Anthony G Pipe, Kerstin I Eder

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

19 Citations (Scopus)
389 Downloads (Pure)


The challenges of robotic software testing extend beyond conventional software testing. Valid, realistic and interesting tests need to be generated for multiple programs and hardware running concurrently, deployed into dynamic environments with people. We investigate the use of Belief-Desire-Intention (BDI) agents as models for test generation, in the domain of human-robot interaction (HRI) in simulations. These models provide rational agency, causality, and a reasoning mechanism for planning, which emulate both intelligent and adaptive robots, as well as smart testing environments directed by humans. We introduce reinforcement learning (RL) to automate the exploration of the BDI models using a reward function based on coverage feedback. Our approach is evaluated using a collaborative manufacture example, where the robotic software under test is stimulated indirectly via a simulated human co-worker. We conclude that BDI agents provide intuitive models for test generation in the HRI domain. Our results demonstrate that RL can fully automate BDI model exploration, leading to very effective coverage-directed test generation.
Original languageEnglish
Title of host publicationProceedings of the 3rd Workshop on Model-Driven Robot Software Engineering (MORSE '16)
EditorsUwe Aßmann, Davide Brugali, Christian Piechnick
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Print)9781450342599
Publication statusPublished - Jul 2016
Event2016 Third Workshop on Model-Driven Robot Software Engineering - Leipzig, Germany
Duration: 1 Jul 20161 Jul 2016
Conference number: 3


Workshop2016 Third Workshop on Model-Driven Robot Software Engineering
Abbreviated titleMORSE 16
Internet address


  • Model-based test generation
  • Belief-Desire-Intention agents
  • Simulation-based testing
  • Human-robot interaction
  • Verification agents
  • Reinforcement learning


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