量化机器人学习与泛化中的演示质量
Quantifying Demonstration Quality for Robot Learning and Generalization
AI总结：
本文提出基于任务表现量化机器人从演示中学习的演示质量，通过用户研究验证任务表现与泛化性能显著相关，并发现用户可分为快速适应者与缓慢适应者两组。
摘要：
从演示中学习（Learning from Demonstration, LfD）旨在通过让不同的终端用户提供演示来教导机器人执行任务，从而实现机器人的普及化。然而，大多数LfD技术假设用户提供的演示是最优的。在实际应用中情况并非总是如此，用户很可能提供质量参差不齐的演示，且质量可能会随专业水平和其他因素而变化。演示质量在机器人学习和泛化中起着至关重要的作用。因此，在将演示用于机器人学习之前，量化所提供演示的质量非常重要。在本文中，我们提出基于演示在学习到的任务中的表现来量化其质量。我们假设任务表现能够指示在类似任务上的泛化表现。所提出的方法在一项用户研究（N = 27）中得到了验证。研究招募了具有不同机器人专业水平的用户，在不同的任务约束下教导PR2机器人执行一项通用任务（按下按钮）。他们在两天内的两个环节中教导机器人，以捕捉他们跨环节的教学行为。任务表现被用来将所提供的演示分类为高质量和低质量集合。结果显示，在所有参与者中，任务表现与泛化表现之间存在显著的Pearson相关系数（R = 0.85，p < 0.0001）。我们还发现用户聚类为两组：从第一个环节就提供高质量演示的用户，被归为快速适应者组；在第一个环节提供低质量演示随后通过练习有所提高的用户，被归为缓慢适应者组。这些结果突显了量化演示质量的重要性，它可以指示用户对该任务的适应水平。
英文摘要：
Learning from Demonstration (LfD) seeks to democratize robotics by enabling diverse end-users to teach robots to perform a task by providing demonstrations. However, most LfD techniques assume users provide optimal demonstrations. This is not always the case in real applications where users are likely to provide demonstrations of varying quality, that may change with expertise and other factors. Demonstration quality plays a crucial role in robot learning and generalization. Hence, it is important to quantify the quality of the provided demonstrations before using them for robot learning. In this paper, we propose quantifying the quality of the demonstrations based on how well they perform in the learned task. We hypothesize that task performance can give an indication of the generalization performance on similar tasks. The proposed approach is validated in a user study (N = 27). Users with different robotics expertise levels were recruited to teach a PR2 robot a generic task (pressing a button) under different task constraints. They taught the robot in two sessions on two different days to capture their teaching behaviour across sessions. The task performance was utilized to classify the provided demonstrations into high-quality and low-quality sets. The results show a significant Pearson correlation coefficient (R = 0.85, p < 0.0001) between the task performance and generalization performance across all participants. We also found that users clustered into two groups: Users who provided high-quality demonstrations from the first session, assigned to the fast-adapters group, and users who provided low-quality demonstrations in the first session and then improved with practice, assigned to the slow-adapters group. These results highlight the importance of quantifying demonstration quality, which can be indicative of the adaptation level of the user to the task.
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