深層強化学習による不整地車両の制御
Control of rough terrain vehicles using deep reinforcement learning
16トン級林業車両を対象に、深層強化学習で知覚・計画・制御を統合し、障害物や27度の斜面を含む不整地を安全かつ効率的に走行させる制御器を実現した。
著者: Viktor Wiberg, Erik Wallin, Martin Servin, Tomas Nordfjell
分類: cs.RO, cs.AI
原文アブストラクト
We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate. This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle with two frame articulation joints, six wheels, and their actively articulated suspensions to traverse rough terrain. The carefully shaped reward signal promotes safe, environmental, and efficient driving, which leads to the emergence of unprecedented driving skills. We test learned skills in a virtual environment, including terrains reconstructed from high-density laser scans of forest sites. The controller displays the ability to handle obstructing obstacles, slopes up to 27$^\circ$, and a variety of natural terrains, all with limited wheel slip, smooth, and upright traversal with intelligent use of the active suspensions. The results confirm that deep reinforcement learning has the potential to enhance control of vehicles with complex dynamics and high-dimensional observation data compared to human operators or traditional control methods, especially in rough terrain.
