mlsanityData to 14 Oct 2026

Precision at Speed: Sample-Efficient Online Model-Based Reinforcement Learning for Hydraulic Excavator Control

arXiv 2609.31025 ↗First posted 25 Sep 2026cs.RO cs.LG cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

Precise, high-speed control remains challenging for robots with complex actuation dynamics. Learning directly on hardware is further constrained by the cost of real-world interaction. We present an online model-based reinforcement learning framework that learns a probabilistic dynamics ensemble model from scratch for sampling-based model predictive control. A precision-gated contouring objective conditions the progress reward on path accuracy, prioritizing precision over speed. In a data-driven excavator simulator, the framework achieves higher sample efficiency than the evaluated model-based reinforcement learning baselines. We validate the framework by learning directly on an 11.5-ton Menzi Muck M445 hydraulic excavator, without demonstrations or simulation pretraining. After 20 minutes of interaction, the controller reaches tracking accuracy comparable to prior learned controllers trained on 100-150 minutes of data. After 40 minutes, it sustains sub-centimeter mean path error at high operating speeds.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 15 of its 27 references that are in the map. First posted on arXiv 25 Sep 2026.

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15 of 27 references.

References · 15

  1. 2018Recurrent World Models Facilitate Policy Evolutioncited by 785
  2. 2018Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Modelscited by 774
  3. 2023TD-MPC2: Scalable, Robust World Models for Continuous Controlcited by 512
  4. 2022Temporal Difference Learning for Model Predictive Controlcited by 397
  5. 2022Learning robust perceptive locomotion for quadrupedal robots in the wildcited by 382
  6. 2017Optimization-Based Autonomous Racing of 1:43 Scale RC Carscited by 50
  7. 2020Learning to Play Table Tennis From Scratch using Muscular Robotscited by 35
  8. 2021Model Predictive Contouring Control for Time-Optimal Quadrotor Flightcited by 21
  9. 2024MPCC++: Model Predictive Contouring Control for Time-Optimal Flight with Safety Constraintscited by 7
  10. 2021Dynamic Modeling of Bucket-Soil Interactions Using Koopman-DFL Lifting Linearization for Model Predictive Contouring Control of Autonomous Excavatorscited by 4
  11. 2019Iterative Learning Control for Fast and Accurate Position Tracking with an Articulated Soft Robotic Armcited by 3
  12. 2025Learning Soft Robotic Dynamics with Active Explorationcited by 2
3 more
  1. 2026Advances, challenges, and opportunities for legged robotscited by 2
  2. 2025Rethinking Reference Trajectories in Agile Drone Racing: A Unified Reference-Free Model-Based Controller via MPPIcited by 1
  3. 2025Efficient Model-Based Reinforcement Learning for Robot Control via Online Optimizationcited by 1

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  1. 2026IL-ACT: Imitation Learning with Adaptive Cartesian Tracking Control for a 30-ton Excavator
  2. 20243D Operation of Autonomous Excavator based on Reinforcement Learning through Independent Reward for Individual Joints
  3. 2024Reinforcement Learning Control for Autonomous Hydraulic Material Handling Machines with Underactuated Tools
  4. 2025ExT: Towards Scalable Autonomous Excavation via Large-Scale Multi-Task Pretraining and Fine-Tuning
  5. 2025Learning to Capture Rocks using an Excavator: A Reinforcement Learning Approach with Guiding Reward Formulation
  6. 2026Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulation
  7. 2026Contraction Metric Based Safe Reinforcement Learning Force Control for a Hydraulic Actuator with Real-World Training
  8. 2026Learning Quadrupedal Locomotion for a Heavy Hydraulic Robot Using an Actuator Model