  Techniques based on Reinforcement Learning (RL) are increasingly being used
to design control policies for robotic systems. RL fundamentally relies on
state-based reward functions to encode desired behavior of the robot and bad
reward functions are prone to exploitation by the learning agent, leading to
behavior that is undesirable in the best case and critically dangerous in the
worst. On the other hand, designing good reward functions for complex tasks is
a challenging problem. In this paper, we propose expressing desired high-level
robot behavior using a formal specification language known as Signal Temporal
Logic (STL) as an alternative to reward/cost functions. We use STL
specifications in conjunction with model-based learning to design model
predictive controllers that try to optimize the satisfaction of the STL
specification over a finite time horizon. The proposed algorithm is empirically
evaluated on simulations of robotic system such as a pick-and-place robotic
arm, and adaptive cruise control for autonomous vehicles.
