Buchumschlag

Reference-Free Formula Drift with Reinforcement Learning: From Driving Data to Tire Energy-Inspired, Real-World Policies

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Djeumou, Franck, Thompson, Michael, Suminaka, Makoto, Subosits, John
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Robotics Machine Learning Systems and Control
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

Risk-Averse Model Predictive Control for Racing in Adverse Conditions
von: Lew, Thomas, et al.
Veröffentlicht: (2024)

First, Learn What You Don't Know: Active Information Gathering for Driving at the Limits of Handling
von: Davydov, Alexander, et al.
Veröffentlicht: (2024)

Control Barrier Functions for Shared Control and Vehicle Safety
von: Dallas, James, et al.
Veröffentlicht: (2025)

Boundary Sampling to Learn Predictive Safety Filters via Pontryagin's Maximum Principle
von: Dallas, James, et al.
Veröffentlicht: (2026)

MPC-Inspired Reinforcement Learning for Verifiable Model-Free Control
von: Lu, Yiwen, et al.
Veröffentlicht: (2023)

Spatial Envelope MPC: High Performance Driving without a Reference
von: Yu, Siyuan, et al.
Veröffentlicht: (2025)

Learning to Drift with Individual Wheel Drive: Maneuvering Autonomous Vehicle at the Handling Limits
von: Zhou, Yihan, et al.
Veröffentlicht: (2025)

Free-Gate: Planning, Control And Policy Composition via Free Energy Gating
von: Rossi, Francesca, et al.
Veröffentlicht: (2024)

Learning Autonomous Race Driving with Action Mapping Reinforcement Learning
von: Wang, Yuanda, et al.
Veröffentlicht: (2024)

Bootstrapping Reinforcement Learning with Sub-optimal Policies for Autonomous Driving
von: Zhang, Zhihao, et al.
Veröffentlicht: (2025)

Autonomous Drifting Based on Maximal Safety Probability Learning
von: Hoshino, Hikaru, et al.
Veröffentlicht: (2024)

A Hierarchical Test Platform for Vision Language Model (VLM)-Integrated Real-World Autonomous Driving
von: Zhou, Yupeng, et al.
Veröffentlicht: (2025)

Tire Wear Aware Trajectory Tracking Control for Multi-axle Swerve-drive Autonomous Mobile Robots
von: Hu, Tianxin, et al.
Veröffentlicht: (2025)

Bayesian Optimization-based Tire Parameter and Uncertainty Estimation for Real-World Data
von: Goblirsch, Sven, et al.
Veröffentlicht: (2025)

Reset-Free Reinforcement Learning for Real-World Agile Driving: An Empirical Study
von: Honda, Kohei, et al.
Veröffentlicht: (2026)

OpenConvoy: Universal Platform for Real-World Testing of Cooperative Driving Systems
von: Burns, Owen, et al.
Veröffentlicht: (2024)

TeraSim-World: Worldwide Safety-Critical Data Synthesis for End-to-End Autonomous Driving
von: Wang, Jiawei, et al.
Veröffentlicht: (2025)

Adaptive Actor-Critic Based Optimal Regulation for Drift-Free Uncertain Nonlinear Systems
von: Dani, Ashwin P., et al.
Veröffentlicht: (2024)

A Deep Reinforcement Learning Based Motion Cueing Algorithm for Vehicle Driving Simulation
von: Scheidel, Hendrik, et al.
Veröffentlicht: (2023)

Reference-Free Sampling-Based Model Predictive Control
von: Schramm, Fabian, et al.
Veröffentlicht: (2025)

Reinforcement Learning Compensated Model Predictive Control for Off-road Driving on Unknown Deformable Terrain
von: Gupta, Prakhar, et al.
Veröffentlicht: (2024)

DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control
von: Malmir, Mohammadhossein, et al.
Veröffentlicht: (2023)

Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions
von: Al-Sharman, Mohammad, et al.
Veröffentlicht: (2024)

Closed-Loop Sim-to-Real Reinforcement Learning for Deformable Microfiber Shape Control
von: Amici, Alessandro, et al.
Veröffentlicht: (2026)

Off Policy Lyapunov Stability in Reinforcement Learning
von: Gill, Sarvan, et al.
Veröffentlicht: (2025)

Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates
von: Zhang, Zixin, et al.
Veröffentlicht: (2025)

Guess the Drift with LOP-UKF: LiDAR Odometry and Pacejka Model for Real-Time Racecar Sideslip Estimation
von: Toschi, Alessandro, et al.
Veröffentlicht: (2024)

Learning to Walk from Three Minutes of Real-World Data with Semi-structured Dynamics Models
von: Levy, Jacob, et al.
Veröffentlicht: (2024)

Certifying Stability of Reinforcement Learning Policies using Generalized Lyapunov Functions
von: Long, Kehan, et al.
Veröffentlicht: (2025)

Towards an Autonomous Test Driver: High-Performance Driver Modeling via Reinforcement Learning
von: Subosits, John, et al.
Veröffentlicht: (2024)

EMATO: Energy-Model-Aware Trajectory Optimization for Autonomous Driving
von: Tian, Zhaofeng, et al.
Veröffentlicht: (2024)

Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning
von: Jonnarth, Arvi, et al.
Veröffentlicht: (2024)

Integrating Model-Based Footstep Planning with Model-Free Reinforcement Learning for Dynamic Legged Locomotion
von: Lee, Ho Jae, et al.
Veröffentlicht: (2024)

Path Planning and Reinforcement Learning-Driven Control of On-Orbit Free-Flying Multi-Arm Robots
von: Belmonte-Baeza, Álvaro, et al.
Veröffentlicht: (2026)

Reward-Augmented Reinforcement Learning for Continuous Control in Precision Autonomous Parking via Policy Optimization Methods
von: Suleman, Ahmad, et al.
Veröffentlicht: (2025)

Multi-Agent Reinforcement Learning-based Cooperative Autonomous Driving in Smart Intersections
von: Yu, Taoyuan, et al.
Veröffentlicht: (2025)

Zero-shot Sim-to-Real Transfer for Reinforcement Learning-based Visual Servoing of Soft Continuum Arms
von: Yang, Hsin-Jung, et al.
Veröffentlicht: (2025)

ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real
von: Yu, Youwei, et al.
Veröffentlicht: (2025)

Learning-Based Robust Control: Unifying Exploration and Distributional Robustness for Reliable Robotics via Free Energy
von: Jesawada, Hozefa, et al.
Veröffentlicht: (2026)

Real-Time Model-Free Deep Reinforcement Learning for Force Control of a Series Elastic Actuator
von: Sambhus, Ruturaj, et al.
Veröffentlicht: (2023)