Real Time Trajectory Prediction Using Deep Conditional Generative Models

Real Time Trajectory Prediction Using Deep Conditional Generative Models Sebastian Gomez-Gonzalez 1, 2, Sergey Prokudin 1, Bernhard Sch olkopf 1 and Jan Peters 2 Abstract -- Data driven methods for time series forecasting that quantify uncertainty open new important possibilities for robot tasks with hard real time constraints, allowing the robot system to make decisions that trade off between reaction time and accuracy in the predictions. Despite the recent advances in deep learning, it is still challenging to make long term accurate predictions with the low latency required by real time robotic systems. In this paper, we propose a deep conditional generative model for trajectory prediction that is learned from a data set of collected trajectories. Our method uses an encoder and decoder deep networks that maps complete or partial trajectories to a Gaussian distributed latent space and back, allowing for fast inference of the future values of a trajectory given previous observations. The encoder and decoder networks are trained using stochastic gradient variational Bayes. In the experiments, we show that our model provides more accurate long term predictions with a lower latency that popular models for trajectory forecasting like recurrent neural networks or physical models based on differential equations. Finally, we test our proposed approach in a robot table tennis scenario to evaluate the performance of the proposed method in a robotic task with hard real time constraints. I. INTRODUCTION Dynamic high speed robotics tasks often require accurate methods to forecast the future value of a physical quantity based on previous measurements while respecting the real time constraints of the particular application.