# Large Scale Robotic Material Handling: Learning, Planning, and Control

arXiv: https://arxiv.org/abs/2508.09003 · HTML: https://mlsanity.com/paper/2508.09003/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2025.

## Built on by (most cited first, 3 of 3)

- [Residual Reinforcement Learning for Waste-Container Lifting Using Large-Scale Cranes with Underactuated Tools](https://mlsanity.com/paper/2602.05895/) (2026)
- [Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulation](https://mlsanity.com/paper/2609.12677/) (2026)
- [PileBelief: Persistent Physical State for Interaction-Driven World Modeling](https://mlsanity.com/paper/2609.22858/) (2026)

## Abstract (arXiv)

Bulk material handling involves the efficient and precise moving of large quantities of materials, a core operation in many industries, including cargo ship unloading, waste sorting, construction, and demolition. These repetitive, labor-intensive, and safety-critical operations are typically performed using large hydraulic material handlers equipped with underactuated grippers. In this work, we present a comprehensive framework for the autonomous execution of large-scale material handling tasks. The system integrates specialized modules for environment perception, pile attack point selection, path planning, and motion control. The main contributions of this work are two reinforcement learning-based modules: an attack point planner that selects optimal grasping locations on the material pile to maximize removal efficiency and minimize the number of scoops, and a robust trajectory following controller that addresses the precision and safety challenges associated with underactuated grippers in movement, while utilizing their free-swinging nature to release material through dynamic throwing. We validate our framework through real-world experiments on a 40 t material handler in a representative worksite, focusing on two key tasks: high-throughput bulk pile management and high-precision truck loading. Comparative evaluations against human operators demonstrate the system's effectiveness in terms of precision, repeatability, and operational safety. To the best of our knowledge, this is the first complete automation of material handling tasks on a full scale.