Autonomous excavation for hard or compact materials, especially irregular rigid objects, is challenging due to high variance of geometric and physical properties of objects, and large resistive force during excavation. In this paper, we propose a novel learning-based excavation planning method for rigid objects in clutter. Our method consists of a convolutional neural network to predict the excavation success and a sampling-based optimization method for planning high-quality excavation trajectories leveraging the learned prediction model. To reduce the sim2real gap for excavation learning, we propose a voxel-based representation of the excavation scene. We perform excavation experiments in both simulation and real world to evaluate the learning-based excavation planners. We further compare with two heuristic baseline excavation planners and one data-driven scene-independent planner. The experimental results show that our method can plan high-quality excavations for rigid objects in clutter and outperforms the baseline methods by large margins. As far as we know, our work presents the first learning-based excavation planner for cluttered and irregular rigid objects.

翻译：由于物体的几何和物理特性差异很大,挖掘过程中的抵抗力很大,因此自主挖掘硬体或紧凑材料,特别是不规则的僵硬物体是具有挑战性的。在本文件中,我们提议对杂乱的硬物体采用一种新的基于学习的挖掘规划方法。我们的方法包括一个革命性神经网络,以预测挖掘的成功,以及一种基于取样的优化方法,以规划高质量的挖掘轨迹,利用已学的预测模型。为了缩小挖掘学习的模拟现实差距,我们提议对挖掘现场进行基于 voxel 的描述。我们在模拟和现实世界中进行挖掘实验,以评价以学习为基础的挖掘规划者。我们进一步比较了两个超大型基线挖掘规划者和一个以数据为驱动的以现场独立的规划者。实验结果显示,我们的方法可以计划用大边距高质量的挖掘,在污染和超越基线方法的僵硬物体。据我们所知,我们的工作展示了第一个以学习为基础的挖掘计划者,用于混合和不规则的僵硬物体。