Multi-robot Adaptive Sampling for Supervised Spatiotemporal Forecasting

Siva Kailas, Wenhao Luo, Katia P. Sycara

Abstract: Learning to forecast spatiotemporal (ST) environmental processes from a sparse set of samples collected autonomously is a difficult task from both a sampling perspective (collecting the best sparse samples) and from a learning perspective (predicting the next timestep). Recent work in spatiotemporal process learning focuses on using deep learning to forecast from dense samples. Moreover, collecting the best set of sparse samples is understudied within robotics. An example of this is robotic sampling for information gathering, such as using UAVs/UGVs for weather monitoring. In this work, we propose a methodology that leverages a neural methodology called Recurrent Neural Processes to learn spatiotemporal environmental dynamics for forecasting from selective samples gathered by a team of robots using a mixture of Gaussian Processes model in an online learning fashion. Thus, we combine two learning paradigms in that we use an active learning approach to adaptively gather informative samples and a supervised learning approach to capture and predict complex spatiotemporal environmental phenomena.