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

TL;DR

This paper presents a novel approach using BDI agents and reinforcement learning to generate realistic and effective tests for robotic software in human-robot interaction scenarios, improving coverage and realism.

Contribution

It introduces the use of BDI agents combined with reinforcement learning for automated, coverage-driven test generation in HRI robotic software testing.

Findings

BDI agents effectively model intelligent and adaptive behaviors for testing.

Reinforcement learning automates BDI exploration, enhancing test coverage.

Approach validated in a collaborative manufacturing simulation.

Abstract

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.