Signal Temporal Logic Neural Predictive Control

TL;DR

This paper introduces a neural network-based predictive control method that directly optimizes STL robustness scores, enabling efficient, scalable, and reliable satisfaction of complex temporal logic specifications in robotic tasks.

Contribution

It presents a novel neural control approach that combines STL robustness maximization with predictive control, outperforming classical and RL methods in speed and accuracy.

Findings

Outperforms classical MPC and STL-solver in satisfaction rate

Achieves 10X-100X faster performance than classical methods

Effective on tasks with complex STL specifications

Abstract

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.