Learning Minimally-Violating Continuous Control for Infeasible Linear Temporal Logic Specifications. (arXiv:2210.01162v2 [cs.RO] UPDATED)

This paper explores continuous-time control synthesis for target-driven
navigation to satisfy complex high-level tasks expressed as linear temporal
logic (LTL). We propose a model-free framework using deep reinforcement
learning (DRL) where the underlying dynamic system is unknown (an opaque box).
Unlike prior work, this paper considers scenarios where the given LTL
specification might be infeasible and therefore cannot be accomplished
globally. Instead of modifying the given LTL formula, we provide a general
DRL-based approach to satisfy it with minimal violation. …

arxiv continuous linear logic temporal