  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. Then, by applying a reward shaping technique, we
develop a modular policy-gradient architecture exploiting the benefits of the
automaton structure to decompose overall tasks and enhance the performance of
learned controllers; (3) by incorporating Gaussian Processes (GPs) to estimate
the uncertain dynamic systems, we synthesize a model-based safe exploration
during the learning process using Exponential Control Barrier Functions (ECBFs)
that generalize systems with high-order relative degrees; (4) to further
improve the efficiency of exploration, we utilize the properties of LTL
automata and ECBFs to propose a safe guiding process. Finally, we demonstrate
the effectiveness of the framework via several robotic environments. We show an
ECBF-based modular deep RL algorithm that achieves near-perfect success rates
and safety guarding with high probability confidence during training.
