RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms

• URL: http://arxiv.org/abs/2501.17311v2
• Date: Thu, 06 Feb 2025 10:59:25 GMT
• Title: RLPP: A Residual Method for Zero-Shot Real-World Autonomous Racing on Scaled Platforms
• Authors: Edoardo Ghignone, Nicolas Baumann, Cheng Hu, Jonathan Wang, Lei Xie, Andrea Carron, Michele Magno,
• Abstract summary: We propose RLPP, a residual RL framework that enhances a Pure Pursuit controller with an RL-based residual. RLPP improves lap times of the baseline controllers by up to 6.37 %, closing the gap to the State-of-the-Art methods by more than 52 %. RLPP is made available as an open-source tool, encouraging further exploration and advancement in autonomous racing research.
• Score: 9.517327026260181
• License:
• Abstract: Autonomous racing presents a complex environment requiring robust controllers capable of making rapid decisions under dynamic conditions. While traditional controllers based on tire models are reliable, they often demand extensive tuning or system identification. Reinforcement Learning (RL) methods offer significant potential due to their ability to learn directly from interaction, yet they typically suffer from the sim-to-real gap, where policies trained in simulation fail to perform effectively in the real world. In this paper, we propose RLPP, a residual RL framework that enhances a Pure Pursuit (PP) controller with an RL-based residual. This hybrid approach leverages the reliability and interpretability of PP while using RL to fine-tune the controller's performance in real-world scenarios. Extensive testing on the F1TENTH platform demonstrates that RLPP improves lap times of the baseline controllers by up to 6.37 %, closing the gap to the State-of-the-Art methods by more than 52 % and providing reliable performance in zero-shot real-world deployment, overcoming key challenges associated with the sim-to-real transfer and reducing the performance gap from simulation to reality by more than 8-fold when compared to the baseline RL controller. The RLPP framework is made available as an open-source tool, encouraging further exploration and advancement in autonomous racing research. The code is available at: www.github.com/forzaeth/rlpp.

Related papers

• FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control [33.547772623142414]
  Reinforcement Learning (RL)-based Traffic Signal Control (TSC) methods raise some serious issues such as high learning cost and poor generalizability. We propose a novel Federated Imitation Learning (FIL)-based framework for multi-intersection TSC, named FitLight. FitLight allows real-time imitation learning and seamless transition to reinforcement learning.
  arXiv  Detail & Related papers  (2025-02-17T15:48:46Z)
• TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning [61.33599727106222]
  TeLL-Drive is a hybrid framework that integrates a Teacher LLM to guide an attention-based Student DRL policy. A self-attention mechanism then fuses these strategies with the DRL agent's exploration, accelerating policy convergence and boosting robustness.
  arXiv  Detail & Related papers  (2025-02-03T14:22:03Z)
• CHEQ-ing the Box: Safe Variable Impedance Learning for Robotic Polishing [5.467140383171385]
  This work presents an experimental demonstration of the hybrid RL algorithm CHEQ for robotic polishing with variable impedance. On hardware, CHEQ achieves effective polishing behaviour, requiring only eight hours of training and incurring just five failures. Results highlight the potential of adaptive hybrid RL for real-world, contact-rich tasks trained directly on hardware.
  arXiv  Detail & Related papers  (2025-01-14T10:13:41Z)
• Autonomous Vehicle Controllers From End-to-End Differentiable Simulation [60.05963742334746]
  We propose a differentiable simulator and design an analytic policy gradients (APG) approach to training AV controllers. Our proposed framework brings the differentiable simulator into an end-to-end training loop, where gradients of environment dynamics serve as a useful prior to help the agent learn a more grounded policy. We find significant improvements in performance and robustness to noise in the dynamics, as well as overall more intuitive human-like handling.
  arXiv  Detail & Related papers  (2024-09-12T11:50:06Z)
• DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning [61.10299147201369]
  This paper introduces a novel autonomous RL approach, called DigiRL, for training in-the-wild device control agents. We build a scalable and parallelizable Android learning environment equipped with a VLM-based evaluator. We demonstrate the effectiveness of DigiRL using the Android-in-the-Wild dataset, where our 1.3B VLM trained with RL achieves a 49.5% absolute improvement.
  arXiv  Detail & Related papers  (2024-06-14T17:49:55Z)
• Deployable Reinforcement Learning with Variable Control Rate [14.838483990647697]
  We propose a variant of Reinforcement Learning (RL) with variable control rate. In this approach, the policy decides the action the agent should take as well as the duration of the time step associated with that action. We show the efficacy of SEAC through a proof-of-concept simulation driving an agent with Newtonian kinematics.
  arXiv  Detail & Related papers  (2024-01-17T15:40:11Z)
• Learning to Fly in Seconds [7.259696592534715]
  We show how curriculum learning and a highly optimized simulator enhance sample complexity and lead to fast training times. Our framework enables Simulation-to-Reality (Sim2Real) transfer for direct control after only 18 seconds of training on a consumer-grade laptop.
  arXiv  Detail & Related papers  (2023-11-22T01:06:45Z)
• Hybrid Reinforcement Learning for Optimizing Pump Sustainability in Real-World Water Distribution Networks [55.591662978280894]
  This article addresses the pump-scheduling optimization problem to enhance real-time control of real-world water distribution networks (WDNs) Our primary objectives are to adhere to physical operational constraints while reducing energy consumption and operational costs. Traditional optimization techniques, such as evolution-based and genetic algorithms, often fall short due to their lack of convergence guarantees.
  arXiv  Detail & Related papers  (2023-10-13T21:26:16Z)
• Zero-Shot Uncertainty-Aware Deployment of Simulation Trained Policies on Real-World Robots [17.710172337571617]
  Deep reinforcement learning (RL) agents tend to make errors when deployed in the real world due to mismatches between the training and execution environments. We propose a novel uncertainty-aware deployment strategy that combines the strengths of deep RL policies and traditional handcrafted controllers. We show promising results on two real-world continuous control tasks, where BCF outperforms both the standalone policy and controller.
  arXiv  Detail & Related papers  (2021-12-10T02:13:01Z)
• Learning from Simulation, Racing in Reality [126.56346065780895]
  We present a reinforcement learning-based solution to autonomously race on a miniature race car platform. We show that a policy that is trained purely in simulation can be successfully transferred to the real robotic setup.
  arXiv  Detail & Related papers  (2020-11-26T14:58:49Z)
• Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot Locomotion [78.46388769788405]
  We introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained policy optimization (CPPO) We show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.
  arXiv  Detail & Related papers  (2020-02-22T10:15:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.