RoboKoop: Efficient Control Conditioned Representations from Visual Input in Robotics using Koopman Operator

TL;DR

This paper introduces RoboKoop, a novel approach that leverages Koopman theory to learn efficient, stable visual representations for robotic control, significantly improving policy learning efficiency and accuracy from high-dimensional visual data.

Contribution

The paper presents a Contrastive Spectral Koopman Embedding network that enables linearized visual representations conditioned on tasks, facilitating reinforcement learning-based control in robotics.

Findings

Outperforms existing methods in control stability and accuracy.

Enhances learning efficiency over extended horizons.

Provides robust visual representations for complex control tasks.

Abstract

Developing agents that can perform complex control tasks from high-dimensional observations is a core ability of autonomous agents that requires underlying robust task control policies and adapting the underlying visual representations to the task. Most existing policies need a lot of training samples and treat this problem from the lens of two-stage learning with a controller learned on top of pre-trained vision models. We approach this problem from the lens of Koopman theory and learn visual representations from robotic agents conditioned on specific downstream tasks in the context of learning stabilizing control for the agent. We introduce a Contrastive Spectral Koopman Embedding network that allows us to learn efficient linearized visual representations from the agent's visual data in a high dimensional latent space and utilizes reinforcement learning to perform off-policy control on top of the extracted representations with a linear controller. Our method enhances stability and control in gradient dynamics over time, significantly outperforming existing approaches by improving efficiency and accuracy in learning task policies over extended horizons.