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Training generative motion models with constraints directly integrated into the objective can drastically reduce collision rates and improve trajectory quality in robot navigation tasks.
Forget RNNs for modeling dynamics: a Kalman filter-VAE hybrid can learn state-space representations that disentangle static and dynamic features, outperforming previous models in prediction accuracy and system identification.
