Safe Reinforcement Learning for Autonomous Navigation of a Driveable Vertical Mast Lift

Abstract

In this work, we consider the issue of controlling a Driveable Vertical Mast Lift (DVML) to autonomously navigate an environment while ensuring required safety constraints. DMVL are industrial vehicles used in several applications, f.i. in logistics and smart agriculture, to allow operators placed in a basket accessing elevated worksites. When driving such machines, operators are exposed to hazards that could lead to potentially serious accidents. Reinforcement Learning (RL) is a data-driven approach that is increasingly being used to control complex systems. This work investigates the advancements in the field of Safe RL from a practical perspective, employing several state-of-art algorithms to equip a DVML with autonomous driving capabilities. We highlight how benchmark environments, while satisfactorily affirming Safe RL methodologies as proof-of-concepts, can widen the gap that prevents such methodologies from both being applied in real scenarios and becoming much more popular in industrial use-cases.