Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems
Shreenabh Agrawal, Hugo T. M. Kussaba, Lingyun Chen, Allen Emmanuel Binny, Pushpak Jagtap, Sami Haddadin, Abdalla Swikir
Abstract
Learning from Demonstration (LfD) techniques enable robots to learn and generalize tasks from user demonstrations, eliminating the need for coding expertise among end-users. One established technique to implement LfD in robots is to encode demonstrations in a stable Dynamical System (DS). However, finding a stable dynamical system entails solving an optimization problem with bilinear matrix inequality (BMI) constraints, a non-convex problem which, depending on the number of scalar constraints and variables, demands significant computational resources and is susceptible to numerical issues such as floating-point errors. To address these challenges, we propose a novel compositional approach that enhances the applicability and scalability of learning stable DSs with BMIs.
BibTeX
@inproceedings{iros2025_scalablelearning,
title = {Scalable Learning of High-Dimensional Demonstrations with Composition of Linear Parameter Varying Dynamical Systems},
author = {Shreenabh Agrawal and Hugo T. M. Kussaba and Lingyun Chen and Allen Emmanuel Binny and Pushpak Jagtap and Sami Haddadin and Abdalla Swikir},
booktitle = {IROS 2025},
year = {2025}
}