Learning from demonstration (LfD) is a widely researched paradigm for teaching robots to perform novel tasks. LfD works particularly well with program synthesis since the resulting programmatic policy is data efficient, interpretable, and amenable to formal verification. However, existing synthesis approaches to LfD rely on precise and labeled demonstrations and are incapable of reasoning about the uncertainty inherent in human decision-making. In this paper, we propose PLUNDER, a new LfD approach that integrates a probabilistic program synthesizer in an expectation-maximization (EM) loop to overcome these limitations. PLUNDER only requires unlabeled low-level demonstrations of the intended task (e.g., remote-controlled motion trajectories), which liberates end-users from providing explicit labels and facilitates a more intuitive LfD experience. PLUNDER also generates a probabilistic policy that captures actuation errors and the uncertainties inherent in human decision making. Our experiments compare PLUNDER with state-of the-art LfD techniques and demonstrate its advantages across different robotic tasks.

翻译：从演示中学习（LfD）是研究机器人执行新任务的广泛范式。与程序合成结合时，LfD表现尤为突出，因为所生成的程序化策略具有数据高效、可解释性强且适用于形式化验证的优点。然而，现有的LfD合成方法依赖精确且标注过的演示，无法推理人类决策中固有的不确定性。本文提出PLUNDER，一种新型LfD方法，它通过期望最大化（EM）循环集成概率程序合成器以克服上述局限。PLUNDER仅需目标任务的未标注低层级演示（如远程控制运动轨迹），从而免去终端用户提供显式标注，带来更直观的LfD体验。同时，PLUNDER生成的概率策略能够捕获执行误差及人类决策中的固有不确定性。实验将PLUNDER与当前最先进的LfD技术进行对比，验证了其在不同机器人任务中的优势。