The efficient exchange of information is an essential aspect of intelligent collective behavior.
Event-triggered control and estimation achieve some efficiency by replacing continuous data …

Machine learning and deep learning have been used extensively to classify physical
surfaces through images and time-series contact data. However, these methods rely on …

Deep reinforcement learning (RL) has shown remarkable success in specific offline decision-
making scenarios, yet its theoretical guarantees are still under development. Existing works …

Mathematical models are fundamental building blocks in the design of dynamical control
systems. As control systems are becoming increasingly complex and networked …

When deploying machine learning algorithms in the real world, guaranteeing safety is an
essential asset. Existing safe learning approaches typically consider continuous variables …

In this paper, we develop a new change detection algorithm for detecting a change in the
Markov kernel over a metric space in which the post-change kernel is unknown. Under the …

Distinguishability and, by extension, observability are key properties of dynamical systems.
Establishing these properties is challenging, especially when no analytical model is …

Sliding a tool across a surface generates rich sensations that can be analyzed to recognize
what is being touched. However, the optimal configuration for capturing these signals is yet …

The focus of this study is to evaluate the effectiveness of Machine Learning (ML) methods for
two-sample testing with right-censored observations. To achieve this, we develop several …

Robust controllers ensure stability in feedback loops designed under uncertainty but at the
cost of performance. Model uncertainty in time-invariant systems can be reduced by recently …