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Found 3 papers, 3 papers with code
Real Time Trajectory Prediction Using Deep Conditional Generative Models
Our method uses encoder and decoder deep networks that maps complete or partial trajectories to a Gaussian distributed latent space and back, allowing for fast inference of the future values of a trajectory given previous observations.
Bayesian Online Prediction of Change Points
Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences.
Adaptation and Robust Learning of Probabilistic Movement Primitives
However, to be able to capture variability and correlations between different joints, a probabilistic movement primitive requires the estimation of a larger number of parameters compared to their deterministic counterparts, that focus on modeling only the mean behavior.
