Safety-Critical Learning of Robot Control with Temporal Logic Specifications

Understand

Reinforcement learning (RL) is a promising approach.

• However, success is limited to real-world applications, because ensuring safe exploration and facilitating adequate exploitation is a challenge for controlling robotic systems with unknown models and measurement uncertainties.
• The learning problem becomes even more difficult for complex tasks over continuous state-action.
• In this paper, we propose a learning-based robotic control framework consisting of several aspects: (1) we leverage Linear Temporal Logic (LTL) to express complex tasks over infinite horizons that are translated to a novel automaton structure; (2) we detail an innovative reward scheme for LTL satisfaction with a probabilistic guarantee.