Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulation

2026-09-11

AI Summary

研究使用强化学习和材料点法模拟解决自主挖掘设备土壤操作问题，通过基于材料状态的控制器实现技能迁移，并在不同规模机器上验证了方法的有效性。

Abstract

Earthmoving tasks such as excavation, backfilling, or embankment construction require deliberate repositioning of deformable soil. For these tasks, human operators use all shovel faces, while autonomous systems so far are limited to excavation and dumping. Current methods often rely on heuristic models but do not incorporate soil mechanics. We address this shortcoming by using Reinforcement Learning in a GPU-parallelized Material Point Method particle simulation. Our controllers are conditioned on material state such as shape and compactness, enabling skills that use multiple contact faces of the tool and displace material both inside and outside of the shovel. To use the same learned weights across machines, our policies operate in a normalized end-effector space and are deployed through a calibrated machine interface. We evaluate this calibrated transfer on an 11.5t hydraulic excavator and a 500g tabletop robot. We validate performance through autonomous construction of a 42m long, 2.1m high embankment in 45min, executing 201 individual policy strokes without failure, retry, or operator intervention. In a direct comparison, the autonomous controller matches an expert operator's progression speed and produces a higher, more consistent embankment. Additional qualitative backfilling and compaction experiments demonstrate the material-state awareness and calibrated transfer across machines.

Problem

• earthmoving tasks
• autonomous systems
• soil mechanics

Innovation

• Reinforcement Learning
• Material Point Method
• material-state awareness
• calibrated transfer