  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.
