论文标题
我为什么失败？一种基于因果的方法来查找机器人失败的解释
Why did I fail? A Causal-based Method to Find Explanations for Robot Failures
论文作者
论文摘要
以人为本的环境中的机器人失败是不可避免的。因此，机器人解释这种失败的能力对于与人类互动以提高信任和透明度至关重要。为了实现这一技能，本文解决的主要挑战是i）获取足够的数据以学习环境的原因效应模型，ii）基于该模型产生因果解释。我们通过从模拟数据中学习因果贝叶斯网络来解决I）。关于II），我们提出了一种新方法，使机器人能够对任务失败产生对比解释。该解释基于设置失败状态与最接近的状态相反，该状态可以成功执行。该状态是通过广度优先搜索发现的，是基于从学到的因果模型的成功预测。我们在两种不同的情况下评估了我们的方法i）堆叠立方体，ii）将球形掉入容器中。获得的因果模型分别达到70％和72％的SIM2REAL精度。我们最终表明，我们的新方法在多个任务上缩放，并允许真正的机器人给出故障解释，例如“上立方体堆叠得太高，太远，距离下部立方体的右边。”
Robot failures in human-centered environments are inevitable. Therefore, the ability of robots to explain such failures is paramount for interacting with humans to increase trust and transparency. To achieve this skill, the main challenges addressed in this paper are I) acquiring enough data to learn a cause-effect model of the environment and II) generating causal explanations based on that model. We address I) by learning a causal Bayesian network from simulation data. Concerning II), we propose a novel method that enables robots to generate contrastive explanations upon task failures. The explanation is based on setting the failure state in contrast with the closest state that would have allowed for a successful execution. This state is found through breadth-first search and is based on success predictions from the learned causal model. We assessed our method in two different scenarios I) stacking cubes and II) dropping spheres into a container. The obtained causal models reach a sim2real accuracy of 70% and 72%, respectively. We finally show that our novel method scales over multiple tasks and allows real robots to give failure explanations like 'the upper cube was stacked too high and too far to the right of the lower cube.'
