Imitation Learning (IL) is a promising paradigm for teaching robots to perform novel tasks using demonstrations. Most existing approaches for IL utilize neural networks (NN), however, these methods suffer from several well-known limitations: they 1) require large amounts of training data, 2) are hard to interpret, and 3) are hard to repair and adapt. There is an emerging interest in programmatic imitation learning (PIL), which offers significant promise in addressing the above limitations. In PIL, the learned policy is represented in a programming language, making it amenable to interpretation and repair. However, state-of-the-art PIL algorithms assume access to action labels and struggle to learn from noisy real-world demonstrations. In this paper, we propose PLUNDER, a novel PIL algorithm that integrates a probabilistic program synthesizer in an iterative Expectation-Maximization (EM) framework to address these shortcomings. Unlike existing PIL approaches, PLUNDER synthesizes probabilistic programmatic policies that are particularly well-suited for modeling the uncertainties inherent in real-world demonstrations. Our approach leverages an EM loop to simultaneously infer the missing action labels and the most likely probabilistic policy. We benchmark PLUNDER against several established IL techniques, and demonstrate its superiority across five challenging imitation learning tasks under noise. PLUNDER policies achieve 95% accuracy in matching the given demonstrations, outperforming the next best baseline by 19%. Additionally, policies generated by PLUNDER successfully complete the tasks 17% more frequently than the nearest baseline.

翻译：模仿学习（IL）是通过演示教机器人执行新任务的一种极具前景的范式。现有大多数IL方法采用神经网络（NN），但这些方法存在几个众所周知的局限性：1) 需要大量训练数据，2) 难以解释，3) 难以修复和适应。针对上述局限，程序化模仿学习（PIL）正受到新兴关注，其显著优势在于将学得的策略以编程语言表示，从而便于解释和修复。然而，现有PIL算法假设能获取动作标签，且难以从含噪的真实世界演示中学习。本文提出PLUNDER——一种新型PIL算法，它通过迭代期望最大化（EM）框架集成概率程序合成器来克服这些缺陷。与现有PIL方法不同，PLUNDER合成的概率化程序策略特别适用于建模真实世界演示中固有的不确定性。我们的方法利用EM循环同时推断缺失的动作标签与最可能的概率策略。我们将PLUNDER与多种成熟IL技术进行基准测试，并在含噪环境下五个具有挑战性的模仿学习任务中证明了其优越性。PLUNDER策略在匹配给定演示时达到95%的准确率，超过次优基线方法19%。此外，PLUNDER生成的策略完成任务的成功率比最近基线方法高出17%。