Probabilistic Human Motion Prediction via A Bayesian Neural Network
This work addresses the need for safer and more efficient human-robot interaction by providing a probabilistic approach to motion prediction, though it is incremental as it extends existing deterministic neural networks.
The paper tackles the problem of human motion prediction by proposing a probabilistic model based on a Bayesian neural network, which generates multiple future motions and calculates uncertainties to improve safety and efficiency in human-robot interaction, showing better performance than deterministic methods on the Human3.6m dataset.
Human motion prediction is an important and challenging topic that has promising prospects in efficient and safe human-robot-interaction systems. Currently, the majority of the human motion prediction algorithms are based on deterministic models, which may lead to risky decisions for robots. To solve this problem, we propose a probabilistic model for human motion prediction in this paper. The key idea of our approach is to extend the conventional deterministic motion prediction neural network to a Bayesian one. On one hand, our model could generate several future motions when given an observed motion sequence. On the other hand, by calculating the Epistemic Uncertainty and the Heteroscedastic Aleatoric Uncertainty, our model could tell the robot if the observation has been seen before and also give the optimal result among all possible predictions. We extensively validate our approach on a large scale benchmark dataset Human3.6m. The experiments show that our approach performs better than deterministic methods. We further evaluate our approach in a Human-Robot-Interaction (HRI) scenario. The experimental results show that our approach makes the interaction more efficient and safer.