# Imitation-Learning Digging Policy

The work presents a learning-based framework for continuous autonomous excavation that integrates terrain-aware target selection with reinforcement- and imitation-learning controllers: a shared task-conditioned RL policy handles waypoint-guided approach and loaded transport, an IL policy learns vision-based digging and lifting from expert demonstrations, and digging targets are selected from LiDAR elevation maps and converted into bucket-tip waypoints; deployed on a scaled hydraulic excavator with multimodal sensing and closed-loop actuator control, offline replay and physical experiments show more consistent target selection, shorter local motion time, and increased payload, with the learned digging policy achieving a mean payload of 6.52 kg per completed cycle versus 2.

The work presents a learning-based framework for continuous autonomous excavation that integrates terrain-aware target selection with reinforcement- and imitation-learning controllers: a shared task-conditioned RL policy handles waypoint-guided approach and loaded transport, an IL policy learns vision-based digging and lifting from expert demonstrations, and digging targets are selected from LiDAR elevation maps and converted into bucket-tip waypoints; deployed on a scaled hydraulic excavator with multimodal sensing and closed-loop actuator control, offline replay and physical experiments show more consistent target selection, shorter local motion time, and increased payload, with the learned digging policy achieving a mean payload of 6.52 kg per completed cycle versus 2.