Autonomous Wheel Loader Navigation Using Goal-Conditioned Actor-Critic MPC
On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic
Abstract (arXiv)
This paper proposes a novel control method for an autonomous wheel loader, enabling time-efficient navigation to an arbitrary goal pose. Unlike prior works which combine high-level trajectory planners with Model Predictive Control (MPC), we directly enhance the planning capabilities of MPC by incorporating a cost function derived from Actor-Critic Reinforcement Learning (RL). Specifically, we first train an RL agent to solve the pose reaching task in simulation, then transfer the learned planning knowledge to an MPC by incorporating the trained neural network critic as both the stage and terminal cost. We show through comprehensive simulations that the resulting MPC inherits the time-efficient behavior of the RL agent, generating trajectories that compare favorably against those found using trajectory optimization. We also deploy our method on a real-world wheel loader, where we demonstrate successful navigation in various scenarios.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 18 of its 31 references that are in the map. First posted on arXiv 24 Sep 2024.
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- 2017Improved Training of Wasserstein GANscited by 2,235
- 2017Hindsight Experience Replaycited by 1,136
- 2018GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Accelerationcited by 609
- 2018Differentiable MPC for End-to-end Planning and Controlcited by 189
- 2021Maximum Entropy RL (Provably) Solves Some Robust RL Problemscited by 176
- 2022Goal-Conditioned Reinforcement Learning: Problems and Solutionscited by 158
- 2019acados: a modular open-source framework for fast embedded optimal controlcited by 68
- 2020Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Functioncited by 49
- 2020Actor-Critic Reinforcement Learning for Control with Stability Guaranteecited by 45
- 2022Real-time Neural-MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platformscited by 41
- 2021Stabilizing Neural Control Using Self-Learned Almost Lyapunov Criticscited by 35
- 2024Lyapunov-stable Neural Control for State and Output Feedback: A Novel Formulationcited by 25
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- 2020HPIPM: a high-performance quadratic programming framework for model predictive controlcited by 23
- 2023Neural Lyapunov Control for Discrete-Time Systemscited by 23
- 2023Learning for CasADi: Data-driven Models in Numerical Optimizationcited by 15
- 2022Autonomous Wheel Loader Trajectory Tracking Control Using LPV-MPCcited by 2
- 2023A Policy Optimization Method Towards Optimal-time Stabilitycited by 1
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