A Continuous Learning Approach for Probabilistic Human Motion Prediction

Human Motion Prediction (HMP) plays a crucial role in safe Human-Robot-Interaction (HRI). Currently, the majority of HMP algorithms are trained by massive pre-collected data. As the training data only contains a few pre-defined motion patterns, these methods cannot handle the unfamiliar motion patterns. Moreover, the pre-collected data are usually non-interactive, which does not consider the real-time responses of collaborators. As a result, these methods usually perform unsatisfactorily in real HRI scenarios. To solve this problem, in this paper, we propose a novel Continual Learning (CL) approach for probabilistic HMP which makes the robot continually learns during its interaction with collaborators. The proposed approach consists of two steps. First, we leverage a Bayesian Neural Network to model diverse uncertainties of observed human motions for collecting online interactive data safely. Then we take Experience Replay and Knowledge Distillation to elevate the model with new experiences while maintaining the knowledge learned before. We first evaluate our approach on a large-scale benchmark dataset Human3.6m. The experimental results show that our approach achieves a lower prediction error compared with the baselines methods. Moreover, our approach could continually learn new motion patterns without forgetting the learned knowledge. We further conduct real-scene experiments using Kinect DK. The results show that our approach can learn the human kinematic model from scratch, which effectively secures the interaction.