用2D激光雷达生成可解释的反事实推理，让机器人决策更透明。
Realistic Counterfactual Explanations for Machine Learning-Controlled Mobile Robots using 2D LiDAR
- 用遗传算法优化几何形状参数，通过射线投射生成逼真激光数据。
- 在真实与仿真环境中验证，能生成逻辑合理且物理可行的反事实场景。
- 适用于调试和改进基于深度强化学习的机器人控制策略。
本文提出一种基于2D LiDAR的新型方法，用于生成机器学习控制移动机器人的现实反事实解释（CFEs）。尽管人工神经网络（ANNs）能从数据中学习先进决策能力，但其常作为黑箱，尤其在安全关键控制中难以解释。为此，我们采用圆形和矩形等简单形状参数化激光雷达空间，通过遗传算法优化参数，并利用射线投射将配置转化为激光雷达数据。该模型无关方法生成的合成激光数据，可实现用户预设的控制输出，同时保持与原始状态的相似性。我们在真实和仿真环境中的TurtleBot3机器人上，基于深度强化学习（DRL）验证了该方法。结果表明，生成的反事实解释逻辑清晰、物理合理，有助于理解与调试DRL代理的决策过程。本工作推动了移动机器人中可解释AI的发展，为理解、调试和优化机器学习驱动的自主控制提供了实用工具。
原文摘要 · Abstract (English)
This paper presents a novel method for generating realistic counterfactual explanations (CFEs) in machine learning (ML)-based control for mobile robots using 2D LiDAR. ML models, especially artificial neural networks (ANNs), can provide advanced decision-making and control capabilities by learning from data. However, they often function as black boxes, making it challenging to interpret them. This is especially a problem in safety-critical control applications. To generate realistic CFEs, we parameterize the LiDAR space with simple shapes such as circles and rectangles, whose parameters are chosen by a genetic algorithm, and the configurations are transformed into LiDAR data by raycasting. Our model-agnostic approach generates CFEs in the form of synthetic LiDAR data that resembles a base LiDAR state but is modified to produce a pre-defined ML model control output based on a query from the user. We demonstrate our method on a mobile robot, the TurtleBot3, controlled using deep reinforcement learning (DRL) in real-world and simulated scenarios. Our method generates logical and realistic CFEs, which helps to interpret the DRL agent's decision making. This paper contributes towards advancing explainable AI in mobile robotics, and our method could be a tool for understanding, debugging, and improving ML-based autonomous control.
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