Stable Motion Primitives via Imitation and Contrastive Learning

Pérez-Dattari, Rodrigo; Kober, J.

doi:10.1109/TRO.2023.3289597

Stable Motion Primitives via Imitation and Contrastive Learning

Title

Stable Motion Primitives via Imitation and Contrastive Learning

Author

Pérez-Dattari, Rodrigo (TU Delft Learning & Autonomous Control)
Kober, J. (TU Delft Learning & Autonomous Control)

Date

2023

Abstract

Learning from humans allows nonexperts to program robots with ease, lowering the resources required to build complex robotic solutions. Nevertheless, such data-driven approaches often lack the ability to provide guarantees regarding their learned behaviors, which is critical for avoiding failures and/or accidents. In this work, we focus on reaching/point-to-point motions, where robots must always reach their goal, independently of their initial state. This can be achieved by modeling motions as dynamical systems and ensuring that they are globally asymptotically stable. Hence, we introduce a novel Contrastive Learning loss for training deep neural networks (DNN) that, when used together with an Imitation Learning loss, enforces the aforementioned stability in the learned motions. Differently from previous work, our method does not restrict the structure of its function approximator, enabling its use with arbitrary DNNs and allowing it to learn complex motions with high accuracy. We validate it using datasets and a real robot. In the former case, motions are two- and four-dimensional, modeled as first- and second-order dynamical systems. In the latter, motions are three, four, and six-dimensional, of first and second order, and are used to control a 7-DoF robot manipulator in its end effector space and joint space.

Subject

Asymptotic stability
Behavioral sciences
Contrastive learning
deep neural networks (DNNs)
Dynamical systems
dynamical systems
imitation learning (IL)
motion primitives
Robots
Stability analysis
Time-varying systems
Trajectory