尺寸无关：面向挖掘机可迁移土料操作的材料状态强化学习
Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulation
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中文总结 AI 辅助
本研究提出基于物质点法模拟和材料状态条件化强化学习的挖掘机土方操作控制器，在归一化末端执行器空间实现跨机器迁移，自主完成42米堤坝构建，性能媲美专家操作员。
中文摘要 AI 辅助
挖掘、回填或筑堤等土方工程需要对可变形土料进行有意的重新定位。对于这些任务，人类操作员会使用铲斗的所有工作面，而目前的自主系统仅限于挖掘和倾倒。现有方法通常依赖启发式模型，但未纳入土力学。我们通过在GPU并行化的物质点法粒子模拟中使用强化学习来解决这一不足。我们的控制器以材料状态（如形状和密实度）为条件，从而能够使用工具的多个接触面，并在铲斗内外移动材料。为了在不同机器上使用相同的学习权重，我们的策略在归一化的末端执行器空间中运行，并通过校准的机器接口进行部署。我们在11.5吨液压挖掘机和500克桌面机器人上评估了这种校准迁移。我们通过自主构建一条42米长、2.1米高的堤坝（耗时45分钟，执行201次策略动作，无失败、重试或操作员干预）来验证性能。在直接对比中，自主控制器达到了专家操作员的推进速度，并产生了更高、更一致的堤坝。额外的定性回填和压实实验证明了材料状态感知和跨机器的校准迁移。
英文摘要
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.
发表机构
- ETH Zürich（苏黎世联邦理工学院）
- Hexagon Innovation Hub GmbH（海克斯康创新中心有限公司）
机构由 AI 辅助整理，请以论文原文为准。
