Learning Complex Motion Plans using Neural ODEs with Safety and Stability Guarantees

TL;DR

This paper introduces a neural ODE-based method for learning complex motion plans that guarantees safety and stability, demonstrated on robot tasks with robustness to disturbances and obstacle avoidance.

Contribution

It presents a novel DS learning framework combining neural ODEs with control theory tools to ensure safety and stability during complex motion planning.

Findings

Outperforms baseline DS learning on LASA dataset

Successfully applied on Franka Emika robot for wiping and stirring

Achieves robustness to perturbations and obstacle safety

Abstract

We propose a Dynamical System (DS) approach to learn complex, possibly periodic motion plans from kinesthetic demonstrations using Neural Ordinary Differential Equations (NODE). To ensure reactivity and robustness to disturbances, we propose a novel approach that selects a target point at each time step for the robot to follow, by combining tools from control theory and the target trajectory generated by the learned NODE. A correction term to the NODE model is computed online by solving a quadratic program that guarantees stability and safety using control Lyapunov functions and control barrier functions, respectively. Our approach outperforms baseline DS learning techniques on the LASA handwriting dataset and complex periodic trajectories. It is also validated on the Franka Emika robot arm to produce stable motions for wiping and stirring tasks that do not have a single attractor, while being robust to perturbations and safe around humans and obstacles.