Precision at Speed: Sample-Efficient Online Model-Based Reinforcement Learning for Hydraulic Excavator Control
Read the original on arXiv Machine Learning →The paper introduces an online model‑based reinforcement learning framework that learns a probabilistic dynamics ensemble from scratch for sampling‑based model predictive control, specifically targeting precise, high‑speed control of hydraulic excavators. A precision‑gated contouring objective prioritizes path accuracy over speed, enabling the system to achieve higher sample efficiency than existing model‑based RL baselines in a data‑driven simulator. The method is validated on an 11.5‑ton Menzi Muck M445 excavator, reaching tracking accuracy comparable to prior controllers after only 20 minutes of real‑world interaction and maintaining sub‑centimeter mean path error at high speeds after 40 minutes.
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