Realistic Counterfactual Explanations for Machine Learning-Controlled Mobile Robots using 2D LiDAR

Black box AI in robots poses safety risks: The paper addresses a critical problem—ML models controlling physical robots often operate as "black boxes," making their decision-making processes opaque in safety-critical applications where understanding why a robot made a particular decision is essential.

Counterfactual explanations make robot decisions interpretable: The researchers developed a method to generate realistic "what-if" scenarios (counterfactual explanations) showing what environmental changes would cause a robot to make different decisions, helping operators and engineers understand the reasoning behind autonomous control choices.

The approach uses simple, understandable representations: Rather than complex technical explanations, the method parameterizes robot sensor data using basic shapes (circles and rectangles), making the explanations more intuitive and actionable for non-experts reviewing robot behavior.

Real-world testing demonstrates practical applicability: The method was validated on an actual TurtleBot3 robot in both simulated and real-world scenarios, suggesting this approach could be practically deployed to debug and improve ML-based autonomous systems rather than remaining theoretical.

This tool supports responsible AI development: By enabling engineers to understand, debug, and improve ML-controlled robots through realistic explanations, this method contributes to building more trustworthy and safer autonomous systems—a key requirement for responsible AI deployment in robotics.