A trustworthy reinforcement learning algorithm should be competent in solving challenging
real-world problems, including {robustly} handling uncertainties, satisfying {safety} …

Abstract We propose Differentiable Temporal Logic (DTL), a model-agnostic framework that
introduces temporal constraints to deep networks. DTL treats the outputs of a network as a …

Alignment of human and autonomous agent values and objectives is vital in human-
autonomy teaming settings which require collaborative action toward a common goal. In …

Learning from demonstration (LfD) has succeeded in tasks featuring a long time horizon.
However, when the problem complexity also includes human-in-the-loop perturbations, state …

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 …

In this paper, we focus on the challenging problem of model predictive control (MPC) for
dynamics systems with high-level tasks formulated as signal temporal logic (STL). The state …

In this letter, a method for learning a recurrent neural network (RNN) controller that
maximizes the robustness of signal temporal logic (STL) specifications is presented. In …

This article studies the planning problem for a robot residing in partially unknown
environments under signal temporal logic (STL) specifications, where most of the existing …

Seamlessly integrating rules in Learning-from-Demonstrations (LfD) policies is a critical
requirement to enable the real-world deployment of AI agents. Recently, Signal Temporal …

Despite the structure of road environments, imposed via geometry and rules, traffic flows
exhibit complex multiagent dynamics. Reasoning about such dynamics is challenging due to …