End-to-End Stable Imitation Learning via Autonomous Neural Dynamic Policies

TL;DR

This paper introduces Autonomous Neural Dynamic Policies (ANDPs), a novel approach combining neural networks and dynamical systems to ensure stable, flexible, and learnable behaviors for robots, even with complex observations like images.

Contribution

The paper proposes ANDPs, a new class of policies that guarantee asymptotic stability while maintaining the flexibility of neural networks for imitation learning.

Findings

ANDPs ensure asymptotic stability in imitation learning tasks.

They perform effectively with image-based observations.

ANDPs combine neural network flexibility with dynamical system stability.

Abstract

State-of-the-art sensorimotor learning algorithms offer policies that can often produce unstable behaviors, damaging the robot and/or the environment. Traditional robot learning, on the contrary, relies on dynamical system-based policies that can be analyzed for stability/safety. Such policies, however, are neither flexible nor generic and usually work only with proprioceptive sensor states. In this work, we bridge the gap between generic neural network policies and dynamical system-based policies, and we introduce Autonomous Neural Dynamic Policies (ANDPs) that: (a) are based on autonomous dynamical systems, (b) always produce asymptotically stable behaviors, and (c) are more flexible than traditional stable dynamical system-based policies. ANDPs are fully differentiable, flexible generic-policies that can be used in imitation learning setups while ensuring asymptotic stability. In this paper, we explore the flexibility and capacity of ANDPs in several imitation learning tasks including experiments with image observations. The results show that ANDPs combine the benefits of both neural network-based and dynamical system-based methods.