We introduce a method to learn policies from expert demonstrations that are interpretable and manipulable. We achieve interpretability by modeling the interactions between high-level actions as an automaton with connections to formal logic. We achieve manipulability by integrating this automaton into planning via Logical Value Iteration, so that changes to the automaton have predictable effects on the learned behavior. These qualities allow a human user to first understand what the model has learned, and then either correct the learned behavior or zero-shot generalize to new, similar tasks. Our inference method requires only low-level trajectories and a description of the environment in order to learn high-level rules. We achieve this by using a deep Bayesian nonparametric hierarchical model. We test our model on several domains of interest and also show results for a real-world implementation on a mobile robotic arm platform for lunchbox-packing and cabinet-opening tasks.

我们介绍了一种从可解释和可操作的专家演示中学习策略的方法。我们通过将高级动作之间的交互建模为与形式逻辑连接的自动机来实现可解释性。我们通过逻辑值迭代将此自动机集成到规划中来实现可操作性，以便对自动机的更改对学习的行为产生可预测的影响。这些特性允许人类用户首先了解模型学到了什么，然后纠正学习到的行为或零样本泛化到新的类似任务。我们的推理方法只需要低级轨迹和环境描述即可学习高级规则。我们通过使用深度贝叶斯非参数分层模型来实现这一点。我们在几个感兴趣的领域测试了我们的模型，并展示了在移动机械臂平台上实际实现午餐盒包装和开柜任务的结果。