Signal Temporal Logic Neural Predictive Control
Yue Meng Chuchu Fan
Ensuring safety and meeting temporal specifications are critical challenges
for long-term robotic tasks. Signal temporal logic (STL) has been widely used
to systematically and rigorously specify these requirements. However,
traditional methods of finding the control policy under those STL requirements
are computationally complex and not scalable to high-dimensional or systems
with complex nonlinear dynamics. Reinforcement learning (RL) methods can learn
the policy to satisfy the STL specifications via hand-crafted or STL-inspired
rewards, but might encounter unexpected behaviors due to ambiguity and sparsity
in the reward. In this paper, we propose a method to directly learn a neural
network controller to satisfy the requirements specified in STL. Our controller
learns to roll out trajectories to maximize the STL robustness score in
training. In testing, similar to Model Predictive Control (MPC), the learned
controller predicts a trajectory within a planning horizon to ensure the
satisfaction of the STL requirement in deployment. A backup policy is designed
to ensure safety when our controller fails. Our approach can adapt to various
initial conditions and environmental parameters. We conduct experiments on six
tasks, where our method with the backup policy outperforms the classical
methods (MPC, STL-solver), model-free and model-based RL methods in STL
satisfaction rate, especially on tasks with complex STL specifications while
being 10X-100X faster than the classical methods.