An Alignment-Based Approach to Learning Motions from Demonstrations

Abstract

Learning from Demonstration (LfD) is a well-studied field shown to provide robots with fundamental motion skills for a variety of domains. Significant research into various branches of LfD (e.g., learned dynamical systems and movement primitives) can generally be classified into those that learn ``time-dependent” or ``time-independent” systems. Each paradigm provides fundamental benefits and drawbacks -- time-independent methods cannot learn overlapping trajectories, while time-dependence can result in undesirable behavior under perturbation. In this paper, we introduce Cluster Alignment for Learned Motions (CALM), an LfD framework dependent upon an alignment with a representative ``mean" trajectory of demonstrated motions rather than pure time- or state-dependence. We also discuss the convergence properties of CALM and introduce an alignment technique able to handle the sudden shifts in alignment possible under perturbation. We show how CALM mitigates the drawbacks of time-dependent and time-independent techniques on 2D datasets and implement our system on a 7-DoF robot learning tasks in three domains.