Safe and stable motion primitives via imitation learning and geometric fabrics

Saray Bakker, Rodrigo Pérez-Dattari, Cosimo Della Santina, Wendelin Boehmer, Javier Alonso-Mora

Keywords: Imitation Learning, Dynamical Systems, Geometric Motion Planning, Fabrics, Movement Primitives

TL;DR: This work introduces TamedPUMA, a safe and stable extension of the imitation learning algorithm PUMA augmented with a recent development in motion planning called geometric fabrics.

Abstract: Using the language of dynamical systems, Imitation learning (IL) provides an intuitive and effective way of teaching stable task-space motions to robots with goal convergence. Yet, these techniques are affected by serious limitations when it comes to ensuring safety and fulfillment of physical constraints. With this work, we propose to solve this challenge via TamedPUMA, an IL algorithm augmented with a recent development in motion planning called geometric fabrics. We explore two variations of this approach, which we name the forcing policy method and the compatible potential method. The result is a stable imitation learning strategy within which we can seamlessly blend geometrical constraints like collision avoidance and joint limits. Beyond providing a theoretical analysis, we demonstrate TamedPUMA with simulated and real-world tasks, including a 7-DoF manipulator.

Submission Number: 32