# Residual Policy Learning for Vehicle Control of Autonomous Racing Cars

arXiv: https://arxiv.org/abs/2302.07035 · HTML: https://mlsanity.com/paper/2302.07035/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Residual Reinforcement Learning for Robot Control](https://mlsanity.com/paper/1812.03201/) (2018)
- [TossingBot: Learning to Throw Arbitrary Objects with Residual Physics](https://mlsanity.com/paper/1903.11239/) (2019)
- [Super-Human Performance in Gran Turismo Sport Using Deep Reinforcement Learning](https://mlsanity.com/paper/2008.07971/) (2020)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing](https://mlsanity.com/paper/2103.04909/) (2021)
- [Residual Policy Learning for Powertrain Control](https://mlsanity.com/paper/2212.07611/) (2022)

## Built on by (most cited first, 6 of 6)

- [Autonomous Driving Small-Scale Cars: A Survey of Recent Development](https://mlsanity.com/paper/2404.06229/) (2024)
- [BeTAIL: Behavior Transformer Adversarial Imitation Learning from Human Racing Gameplay](https://mlsanity.com/paper/2402.14194/) (2024)
- [RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms](https://mlsanity.com/paper/2501.17311/) (2025)
- [Drive Fast, Learn Faster: On-Board RL for High Performance Autonomous Racing](https://mlsanity.com/paper/2505.07321/) (2025)
- [Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimization](https://mlsanity.com/paper/2603.12960/) (2026)
- [Stability-aware Residual Reinforcement Learning Framework for Robotic Manipulator Disturbance Compensation](https://mlsanity.com/paper/2609.21307/) (2026)

## Abstract (arXiv)

The development of vehicle controllers for autonomous racing is challenging because racing cars operate at their physical driving limit. Prompted by the demand for improved performance, autonomous racing research has seen the proliferation of machine learning-based controllers. While these approaches show competitive performance, their practical applicability is often limited. Residual policy learning promises to mitigate this drawback by combining classical controllers with learned residual controllers. The critical advantage of residual controllers is their high adaptability parallel to the classical controller's stable behavior. We propose a residual vehicle controller for autonomous racing cars that learns to amend a classical controller for the path-following of racing lines. In an extensive study, performance gains of our approach are evaluated for a simulated car of the F1TENTH autonomous racing series. The evaluation for twelve replicated real-world racetracks shows that the residual controller reduces lap times by an average of 4.55 % compared to a classical controller and even enables lap time gains on unknown racetracks.