The increasing level of autonomy of robots poses challenges of trust and social acceptance, especially in human-robot interaction scenarios. This requires an interpretable implementation of robotic cognitive capabilities, possibly based on formal methods as logics for the definition of task specifications. However, prior knowledge is often unavailable in complex realistic scenarios. In this paper, we propose an offline algorithm based on inductive logic programming from noisy examples to extract task specifications (i.e., action preconditions, constraints and effects) directly from raw data of few heterogeneous (i.e., not repetitive) robotic executions. Our algorithm leverages on the output of any unsupervised action identification algorithm from video-kinematic recordings. Combining it with the definition of very basic, almost task-agnostic, commonsense concepts about the environment, which contribute to the interpretability of our methodology, we are able to learn logical axioms encoding preconditions of actions, as well as their effects in the event calculus paradigm. Since the quality of learned specifications depends mainly on the accuracy of the action identification algorithm, we also propose an online framework for incremental refinement of task knowledge from user feedback, guaranteeing safe execution. Results in a standard manipulation task and benchmark for user training in the safety-critical surgical robotic scenario, show the robustness, data- and time-efficiency of our methodology, with promising results towards the scalability in more complex domains.

翻译：随着机器人自主性水平的不断提高，信任与社会接受度方面的挑战日益凸显，尤其是在人机交互场景中。这要求机器人认知能力的实现应具备可解释性，可能需基于形式化方法（如用于任务规约定义的逻辑系统）。然而，在复杂的现实场景中，先验知识往往难以获取。本文提出一种基于噪声示例归纳逻辑编程的离线算法，可直接从少量异构（即非重复性）机器人执行原始数据中提取任务规约（包括动作前提条件、约束条件及效果）。该算法可利用任何基于视频-运动记录的无监督动作识别算法的输出结果。通过将其与环境中非常基础、几乎与任务无关的常识性概念定义相结合（这些概念有助于提升方法的可解释性），我们能够学习编码动作前提条件及其在事件演算范式中效果的逻辑公理。由于学习所得规约的质量主要取决于动作识别算法的准确性，我们还提出一个在线框架，通过用户反馈实现任务知识的增量优化，并保障安全执行。在标准操作任务及面向安全关键型手术机器人场景的用户训练基准测试中，实验结果验证了本方法在鲁棒性、数据效率与时间效率方面的优势，为在更复杂领域中的可扩展性展现了良好前景。