从目标选择到挖掘：一种基于学习的连续自主挖掘框架
From Target Selection to Digging: A Learning-Based Framework for Continuous Autonomous Excavation
- Liaoning University（辽宁大学）
- Northeastern University（东北大学）
- KTH Royal Institute of Technology（瑞典皇家理工学院）
- Tsinghua University（清华大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一种基于学习的连续自主挖掘框架，集成地形感知目标选择与强化/模仿学习控制，在缩比液压挖掘机上实现更一致的目标选择、更短的局部运动时间和更高的有效载荷。
AI中文摘要：
重复挖掘不断重塑料堆几何形状，要求自主挖掘机适应其挖掘目标并在连续挖掘周期中协调运动。我们提出了一种用于连续自主挖掘的基于学习的框架，该框架将地形感知的目标选择与强化学习和模仿学习控制器相结合。该框架将目标条件运动与局部挖掘分离：共享的任务条件强化学习策略控制航点引导的接近和装载运输，而模仿学习策略则从专家演示中学习基于视觉的挖掘和提升。挖掘目标从激光雷达高程图中选择，并转换为用于运动控制的铲斗尖端航点。控制架构通过共享的运动接口协调学习策略和确定性卸载。完整系统部署在一台具有多模态感知和闭环执行器控制的缩比液压挖掘机上。离线回放和物理实验表明，与各自的基线相比，目标选择更一致、局部运动时间更短、有效载荷更高。学习到的挖掘策略每个完成周期的平均有效载荷为6.52公斤，而固定挖掘为2.68公斤。三次五次铲斗运行进一步证明了在连续变化的料堆几何形状下的连续自主挖掘。
英文摘要：
Repeated excavation continuously reshapes pile geometry, requiring an autonomous excavator to adapt its digging targets and coordinate motion across successive excavation cycles. We present a learning-based framework for continuous autonomous excavation that integrates terrain-aware target selection with reinforcement- and imitation-learning controllers. The framework separates target-conditioned motion from local digging: a shared task-conditioned RL policy controls waypoint-guided approach and loaded transport, while an IL policy learns vision-based digging and lifting from expert demonstrations. Digging targets are selected from LiDAR elevation maps and converted into bucket-tip waypoints for motion control. The control architecture coordinates the learned policies and deterministic unloading through a shared motion interface. The complete system is deployed on a scaled hydraulic excavator with multimodal sensing and closed-loop actuator control. Offline replay and physical experiments demonstrate more consistent target selection, shorter local motion time, and increased payload compared with the respective baselines. The learned digging policy achieves a mean payload of 6.52 kg per completed cycle, compared with 2.68 kg for Fixed Dig. Three five-scoop runs further demonstrate consecutive autonomous excavation under continuously changing pile geometry.
