TaskNet: A Neural Task Planner for Autonomous Excavator

Abstract

We present a novel task planner - TaskNet for an autonomous excavator based on a data-driven method, which plans feasible task-level sequence by learning from demonstration data. Given a high-level excavation objective, our TaskNet planner can decompose it into sub-tasks, each of which can be further decomposed into task primitives with specifications. We train our TaskNet using an excavation trace generator and evaluate its performance using a 3D physically-based terrain and excavator simulator. As compared to imitation learning-based methods, the experimental results show that TaskNet can effectively learn task decomposition strategies. The resulting sequences of task primitives can be used as inputs by any excavator motion planner for generating feasible joint-level trajectories. We further validate TaskNet on a state-of-the-art autonomous excavator hardware and software system. The 49-ton autonomous excavator can successfully perform material loading tasks.