Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds
significant promise for capturing expert motor skills through efficient imitation, facilitating …

Data-driven models are subject to model errors due to limited and noisy training data. Key to
the application of such models in safety-critical domains is the quantification of their model …

Combining control engineering with nonparametric modeling techniques from machine
learning allows for the control of systems without analytic description using data-driven …

Real-world autonomous systems are often controlled using conventional model-based
control methods. But if accurate models of a system are not available, these methods may be …

Learning a stable autonomous dynamic system (ADS) encoding human motion rules has
been shown as an effective way for demonstration learning. However, the stability guarantee …

Recurrent neural networks (RNNs) are a class of nonlinear dynamical systems often used to
model sequence-to-sequence maps. RNNs have excellent expressive power but lack the …

Energy consumption in buildings, both residential and commercial, accounts for
approximately 40% of all energy usage in the United States, and similar numbers are being …

In this paper, using the ball and beam system as an illustration, a control scheme is
developed on human-robot collaboration, ie, a two-level hierarchical framework is proposed …

In this paper, we propose a parametric system identification approach for a class of
continuous-time Lur'e-type systems. Using the Mixed-Time-Frequency (MTF) algorithm, we …

Accurate models of mechanical system dynamics are often critical for model‐based control
and reinforcement learning. Fully data‐driven dynamics models promise to ease the process …