In this work, we consider the problem of autonomous exploration in searc...
Temporal logics (TLs) have been widely used to formalize interpretable t...
We develop a novel framework to assess the risk of misperception in a tr...
Machine learning techniques using neural networks have achieved promisin...
This paper explores continuous-time control synthesis for target-driven
...
This work presents a step towards utilizing incrementally-improving symb...
We present a Deep Reinforcement Learning (DRL) algorithm for a task-guid...
Real-time and human-interpretable decision-making in cyber-physical syst...
Time-series data classification is central to the analysis and control o...
Reinforcement learning (RL) is a promising approach and has limited succ...
Transporting objects using quadrotors with cables has been widely studie...
In this paper, we introduce an automata-based framework for planning wit...
Many autonomous systems, such as robots and self-driving cars, involve
r...
This paper presents a novel two-level control architecture for a fully
a...
In this work, we focus on decomposing large multi-agent path planning
pr...
We propose a new robustness score for continuous-time Signal Temporal Lo...
We present a new average-based robustness score for Signal Temporal Logi...
Signal Temporal Logic (STL) is a formal language for describing a broad ...
Reinforcement learning (RL) depends critically on the choice of reward
f...