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Enhanced Human-Robot Collaboration using Constrained Probabilistic Human-Motion Prediction
We propose a novel human motion prediction framework that incorporates human joint constraints and scene constraints in a Gaussian Process Regression (GPR) model to predict human motion over a set time horizon.
MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation
We find that the addition of a symbolic search for basis functions improves the accuracy of the estimation at a reasonable additional computational cost.
GSR: A Generalized Symbolic Regression Approach
Identifying the mathematical relationships that best describe a dataset remains a very challenging problem in machine learning, and is known as Symbolic Regression (SR).
Reliable Neural Networks for Regression Uncertainty Estimation
While deep neural networks are highly performant and successful in a wide range of real-world problems, estimating their predictive uncertainty remains a challenging task.
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