Learning From Multi-Quality Demonstrations in Dynamic Movement Primitives

Learning from Demonstrations (LfD) is an attractive paradigm in robot learning, enabling robots to acquire skills by observing human demonstrations without explicit programming. However, existing LfD approaches typically assume that users provide ideal demonstrations, which rarely holds in practice, especially as non-expert inputs often vary in quality. Such multi-quality demonstrations can cause instability in LfD models and produce outputs that deviate from the desired behavior. To address this, recent studies have improved high-level LfD approaches with notable success, whereas low-level approaches represented by the Dynamic Movement Primitives (DMPs) have received limited attention. In this letter, we propose a novel method that enables DMP to effectively learn from multi-quality demonstrations, capturing user intents and mitigating quality inconsistencies. Specifically, the proposed method combines Dynamic Time Warping (DTW) with a representation learning model (TS2Vec) for unsupervised identification, estimating the relative qualities of DMP's forcing terms and assigning scores. Then, when modeling these terms with a Gaussian Mixture Model, we introduce a latent variable representing the desired forcing term and formulate a weighted joint Maximum A Posteriori objective, enabling reliable modeling guided by the identified scores. Simulation and experimental results show that our method enables DMPs to produce outputs closer to the desired behavior, with improvements in compactness ($18\times$), smoothness ($20\times$), and similarity to the desired demo ($5\times$).