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Found 7 papers, 1 papers with code
Seeing the whole picture instead of a single point: Self-supervised likelihood learning for deep generative models
Recent findings show that deep generative models can judge out-of-distribution samples as more likely than those drawn from the same distribution as the training data.
Imitating by generating: deep generative models for imitation of interactive tasks
To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals.
A Probabilistic Semi-Supervised Approach to Multi-Task Human Activity Modeling
Therefore, video-based human activity modeling is concerned with a number of tasks such as inferring current and future semantic labels, predicting future continuous observations as well as imagining possible future label and feature sequences.
Detect, anticipate and generate: Semi-supervised recurrent latent variable models for human activity modeling
In this work we introduce semi-supervised variational recurrent neural networks which are able to a) model temporal distributions over latent factors and the observable feature space, b) incorporate discrete labels such as activity type when available, and c) generate possible future action sequences on both feature and label level.
Anticipating many futures: Online human motion prediction and synthesis for human-robot collaboration
Fluent and safe interactions of humans and robots require both partners to anticipate the others' actions.
Deep representation learning for human motion prediction and classification
To quantify the learned features, we use the output of different layers for action classification and visualize the receptive fields of the network units.
A Sensorimotor Reinforcement Learning Framework for Physical Human-Robot Interaction
Modeling of physical human-robot collaborations is generally a challenging problem due to the unpredictive nature of human behavior.
