Probabilistic Human Motion Prediction via A Bayesian Neural Network

TL;DR

This paper introduces a Bayesian neural network for probabilistic human motion prediction, enabling robots to assess uncertainty and improve safety and efficiency in human-robot interactions.

Contribution

The paper extends deterministic neural networks to Bayesian models for human motion prediction, incorporating uncertainty estimation for safer robot decision-making.

Findings

Outperforms deterministic methods on Human3.6m dataset

Provides uncertainty estimates for motion predictions

Enhances safety and efficiency in HRI scenarios

Abstract

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.