# Residual Reinforcement Learning for Robot Control

## TL;DR

This paper introduces a residual reinforcement learning approach that combines traditional control methods with RL to effectively handle complex robot control tasks involving contacts and friction, demonstrated on a real-world assembly task.

## Contribution

The paper presents a novel residual RL framework that decomposes control problems into traditional control and RL components, improving robustness in contact-rich environments.

## Key findings

- Successfully applied to real-world block assembly with contacts
- Outperforms purely RL or traditional control methods
- Effective in handling unstable objects and friction

## Abstract

Conventional feedback control methods can solve various types of robot control problems very efficiently by capturing the structure with explicit models, such as rigid body equations of motion. However, many control problems in modern manufacturing deal with contacts and friction, which are difficult to capture with first-order physical modeling. Hence, applying control design methodologies to these kinds of problems often results in brittle and inaccurate controllers, which have to be manually tuned for deployment. Reinforcement learning (RL) methods have been demonstrated to be capable of learning continuous robot controllers from interactions with the environment, even for problems that include friction and contacts. In this paper, we study how we can solve difficult control problems in the real world by decomposing them into a part that is solved efficiently by conventional feedback control methods, and the residual which is solved with RL. The final control policy is a superposition of both control signals. We demonstrate our approach by training an agent to successfully perform a real-world block assembly task involving contacts and unstable objects.