Deep reinforcement learning (DRL) has empowered a variety of artificial intelligence fields,
including pattern recognition, robotics, recommendation systems, and gaming. Similarly …

Thermodynamics could be seen as an expression of physics at a high epistemic level. As
such, its potential as an inductive bias to help machine learning procedures attain accurate …

Machine learning is rapidly advancing nearly every field of science and engineering, and
control theory is no exception. In particular, it has shown incredible promise for handling …

Learning-based controllers, such as neural network (NN) controllers, can show high
empirical performance but lack formal safety guarantees. To address this issue, control …

Deep neural networks (DNNs) training can be difficult due to vanishing and exploding
gradients during weight optimization through backpropagation. To address this problem, we …

Accurate models of robot dynamics are critical for safe and stable control and generalization
to novel operational conditions. Hand-designed models, however, may be insufficiently …

We address the problem of designing stabilizing control policies for nonlinear systems in
discrete-time, while minimizing an arbitrary cost function. When the system is linear and the …

We present a framework and algorithms to learn controlled dynamics models using neural
stochastic differential equations (SDEs)--SDEs whose drift and diffusion terms are both …

This paper presents LEMURS, an algorithm for learning scalable multi-robot control policies
from cooperative task demonstrations. We propose a port-Hamiltonian description of the …

In this paper, we aim to find the conditions for input-state stability (ISS) and incremental input-
state stability (δISS) of Gated Graph Neural Networks (GGNNs). We show that this recurrent …