FMitF: Track I: Program Synthesis for Robot Learning from Demonstrations

随着机器人变得越来越普遍，能力越来越强，这类消费类机器人的最终用户将不可避免地期望能够教机器人如何执行新的任务。从演示中学习(简称LFD)是这个问题的一个流行范例，其中用户演示如何执行任务，而机器人学习一种策略，该策略捕获完成任务要执行的操作序列。大多数现有的LFD技术依赖于神经网络来学习这样的策略。虽然这种技术在某些情况下很有前途，但也存在关键限制，例如需要大量的训练数据，并且缺乏可解释性。该项目的创新之处在于通过将神经网络(对感知任务非常有效)与擅长推理技能的符号学习相结合，解决了机器人LFD的这些限制。该项目的影响是：1)引入一种新的语言，无缝地合并由神经和符号组成的学习程序；2)保证学习的程序满足预期的正确性概念；3)允许使用更真实、更嘈杂的真实世界数据进行这种学习。该项目的贡献还包括对学生的培训和指导，开发将机器人学与正式方法相结合的新型教学课程，并使机器人的学习更具可扩展性、安全性和可解释性。这个项目的研究目标是开发一种基于程序综合的新的LFD范式，目标是将机器人的学习置于更正式、更可解释的基础上，并且减少数据饥饿。该项目的关键智力价值在于开发了一套基于程序综合的新的基础LFD技术。该项目将推动机器人从演示中学习的最先进水平，使其能够以数据高效的方式学习可解释和可验证的程序性政策。该项目还将通过开发针对机器人领域的独特挑战的新技术，包括噪声和高维传感器数据以及与环境的不确定相互作用，来推进程序合成的最先进技术。此外，该项目还将通过考虑所需的正确性标准来推进验证学习的最新技术。最后，该项目将通过在没有从州到高级机器人操作的映射的情况下学习机器人执行策略，在从未标记的示范中学习领域取得进展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As robots become more widely available and more capable, end-users of such consumer robots will inevitably expect to be able to teach robots how to perform new tasks. Learning from demonstration (or LfD, for short) is a popular paradigm for this problem, where a user demonstrates how to perform the task, and the robot learns a policy that captures what sequence of actions to perform to complete the task. Most existing LfD techniques rely on neural networks to learn such policies. While promising in some settings, such techniques suffer from key limitations, such as requiring large amounts of training data and lacking interpretability. This project's novelties are in addressing these limitations for robot LfD by combining neural networks (which are very effective for perception tasks) with symbolic learning, which excels at reasoning skills. The project's impacts are 1) introducing a new language to seamlessly merge learning programs consisting of both neural- and symbolic- components, 2) providing guarantees that the learned programs satisfy desired notions of correctness, and 3) allowing such learning to be performed with more realistic, noisy, real-world data. The project's contributions also include training and mentoring of students, developing novel teaching curriculum that integrate robotics with formal methods, and empowering more scalable, safe, and interpretable learning for robots. The research objective of this project is to develop a new LfD paradigm based on program synthesis, with the goal of putting robot learning on a more formal, interpretable, and less data-hungry footing. The key intellectual merit of the project lies in the development of a new set of foundational LfD techniques based on program synthesis. The project will advance the state-of-the-art in robot learning from demonstration by making it possible to learn, in a data-efficient way, programmatic policies that are interpretable and verifiable. The project will also advance the state-of-the-art in program synthesis by developing novel techniques that target the unique challenges of the robotics domain, including noisy and high-dimensional sensor data and uncertain interactions with the environment. In addition, the project will also advance the state-of-the-art in verified learning by considering desired correctness criteria. Finally, the project will make advances in the field of learning from unlabeled demonstrations by learning robot execution policies in the absence of a mapping from states to high-level robot actions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.