Explainability, in particular, the ability for robots to explain why they have made a decision or behaved in a certain way, is a critical tool in helping users understand the robots they interact and coexist with. Behaviour trees are a popular framework for controlling the decision-making of robots, and thus a natural question to ask is whether or not a system driven by a behaviour tree is capable of answering "why" questions. While explainability for behaviour tree-driven robots has seen some prior attention, no existing methods are capable of generating causal, counterfactual explanations which detail the reasons for robot decisions and behaviour. Therefore, in this work, we introduce a novel approach which automatically generates counterfactual explanations in response to contrastive "why" questions. Our method achieves this by first automatically building a causal model from the structure of the behaviour tree as well as domain knowledge about the state and individual behaviour tree nodes. The resultant causal model is then queried and searched to find a set of diverse counterfactual explanations. We demonstrate that our approach is able to correctly explain the behaviour of a wide range of behaviour tree structures and states in real time, unlike previous methods which are either unable to answer contrastive questions with causal explanations, or are not guaranteed to provide consistent and accurate explanations. By being able to answer a wide range of causal queries, our approach represents a step towards more transparent, understandable, and ultimately safe and trustworthy robotic systems.

翻译：可解释性，特别是机器人解释其为何做出决策或以某种方式行为的能力，是帮助用户理解其交互和共存的机器人的关键工具。行为树是控制机器人决策的流行框架，因此一个自然的问题是，由行为树驱动的系统是否能够回答“为什么”的问题。尽管行为树驱动机器人的可解释性已受到一些关注，但现有方法无法生成因果性反事实解释来详细说明机器人决策和行为的原因。因此，在本工作中，我们引入了一种新方法，能够自动生成应对对比性“为什么”问题的反事实解释。我们的方法首先从行为树的结构以及关于状态和单个行为树节点的领域知识中自动构建因果模型。然后，对所得因果模型进行查询和搜索，以找到一组多样化的反事实解释。我们证明了该方法能够实时正确解释广泛行为树结构和状态的行为，而以往的方法要么无法用因果解释回答对比性问题，要么无法保证提供一致且准确的解释。通过能够回答广泛的因果查询，我们的方法朝着更透明、更可理解、最终更安全且更值得信赖的机器人系统迈出了一步。