Model Learning and Predictive Control for Autonomous Obstacle Reduction via Bulldozing

Abstract

We investigate how employing model learning methods in concert with model predictive control (MPC) can be used to automate obstacle reduction to mitigate risks to Combat Engineers operating construction equipment in an active battlefield. We focus on the task of earthen berm removal using a bladed vehicle. We introduce a novel data-driven formulation for earthmoving dynamics that enables prediction of the vehicle and detailed terrain state over a one second horizon. In a simulation environment, we first record demonstrations from a human operator and then train two different earthmoving models to produce predictions of the high-dimensional state using under six minutes of data. Optimization over the learned model is performed to select an action sequence, constrained to a 2D space of template action trajectories. Simple recovery controllers are implemented to improve controller performance when the model predictions degrade. This system yields near human-level performance on a berm removal task, indicating that model learning and predictive control is a promising data-efficient approach to autonomous earthmoving.