Learning from Demonstration (LfD) enables intuitive robot skill acquisition by allowing robots to learn directly from human task demonstrations. However, current methods often fail to address the fact that due to suboptimal and inconsistent human behavior, the quality of the demonstration can vary within each demonstration. Therefore, we introduce LOPAL (LOcal Performance-aware Active Learning), an active learning approach that leverages this local demonstration quality information. Our approach consists of two synergistic components. First, a local performance-driven LfD method uses a Gaussian Mixture Model (GMM) to encode both the demonstrated trajectories and their associated local quality assessments. This enables the generation of trajectories that outperform the imperfect demonstrations by utilizing complementary local data of high performance. Second, active data acquisition allows to improve beyond the imperfect demonstrations by collecting additional informative samples. In areas missing good data, the user is actively requested to provide corrections through a shared autonomy (SA) mechanism, while the robot autonomously executes the learned behavior. The efficacy of LOPAL was validated in both a simulation and a real-world experiment. The results from a real-world pipe inspection task showed that the proposed approach can achieve up to 27.31 % improvement in task performance while also reducing the effort required to collect the demonstrations.

翻译：从示范中学习（LfD）通过使机器人直接从人类任务示范中学习，实现了直观的机器人技能获取。然而，当前方法通常未能解决一个事实：由于次优且不一致的人类行为，每个示范内部的示范质量可能存在差异。为此，我们提出了LOPAL（局部性能感知的主动学习），一种利用这种局部示范质量信息的主动学习方法。该方法由两个协同组成部分构成。首先，一种局部性能驱动的LfD方法使用高斯混合模型（GMM）对示范轨迹及其相关的局部质量评估进行编码。通过利用互补的高性能局部数据，该方法能够生成优于不完美示范的轨迹。其次，主动数据采集通过收集更多信息样本，允许提升超越不完美示范。在缺乏优质数据的区域，通过共享控制（SA）机制主动要求用户提供修正，同时机器人自主执行已习得的行为。LOPAL的有效性在仿真和真实世界实验中均得到了验证。一项真实管道检测任务的结果表明，所提出的方法在实现任务性能提升高达27.31%的同时，还降低了收集示范所需的工作量。