Hierarchical Proportion Models for Motion Generation via Integration of Motion Primitives

TL;DR

This paper introduces a hierarchical imitation learning framework that combines motion primitives with proportion-based synthesis, enhancing data efficiency and adaptability for complex robot motions.

Contribution

It proposes a novel hierarchical IL approach with three variants, improving motion generation stability and adaptability in robotic tasks.

Findings

Sampling-based and playback-based models outperform standard hierarchical models.

Proportion-based integration enables generation of complex motions beyond primitive set.

Models demonstrate effectiveness in real-robot pick-and-place tasks.

Abstract

Imitation learning (IL) enables robots to acquire human-like motion skills from demonstrations, but it still requires extensive high-quality data and retraining to handle complex or long-horizon tasks. To improve data efficiency and adaptability, this study proposes a hierarchical IL framework that integrates motion primitives with proportion-based motion synthesis. The proposed method employs a two-layer architecture, where the upper layer performs long-term planning, while a set of lower-layer models learn individual motion primitives, which are combined according to specific proportions. Three model variants are introduced to explore different trade-offs between learning flexibility, computational cost, and adaptability: a learning-based proportion model, a sampling-based proportion model, and a playback-based proportion model, which differ in how the proportions are determined and whether the upper layer is trainable. Through real-robot pick-and-place experiments, the proposed models successfully generated complex motions not included in the primitive set. The sampling-based and playback-based proportion models achieved more stable and adaptable motion generation than the standard hierarchical model, demonstrating the effectiveness of proportion-based motion integration for practical robot learning.