# Autonomous Excavation

An LLM-directed excavator can execute mechanically feasible motion whose destination or authority no longer matches the operator's approval. The work binds authorization to the exact motion: a one-use lease ties the action, generated motion, mission contract and admission tolerance together, atomic dispatch rechecks current authority and measured joint drift, and revocation requests a carry-pitch quintic stop. Across a 672-case interface suite the gateway admitted all 120 benign cases and produced zero unauthorized dispatches in the attack suite; in synchronous simulation, dump-phase revocation cut maximum closed-site deposition from 1.23 to 0.33 cubic meters.

An LLM-directed excavator can execute mechanically feasible motion whose destination or authority no longer matches the operator's approval. The work binds authorization to the exact motion: a one-use lease ties the action, generated motion, mission contract and admission tolerance together, atomic dispatch rechecks current authority and measured joint drift, and revocation requests a carry-pitch quintic stop. Across a 672-case interface suite the gateway admitted all 120 benign cases and produced zero unauthorized dispatches in the attack suite; in synchronous simulation, dump-phase revocation cut maximum closed-site deposition from 1.23 to 0.33 cubic meters.

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.