Exact-likelihood User Intention Estimation for Scene-compliant Shared-control Navigation

Abstract

A predictive model for mobility systems capable of understanding the trajectory a user intends to follow in the environment is proposed. Understanding user intention is paramount for any shared-control navigation strategy between a user and an active robotic agent. Equally important however is being able to go beyond simple sample generation to assign probabilistic meaning to the set of possible future trajectories, so most likely scenarios can be assumed. The framework estimates a distribution over possible intentions, proposing a novel generative model predicated on Normalizing Flows which accounts for past behaviours, as traditionally reported in the literature, but also incorporates visual scene information. As the model permits trajectories to be assigned exact likelihoods, tractable density estimates can be readily exploited to finalize an executable intention. Baseline comparisons with the publicly available and widely used KITTI navigational dataset show significant improvements (up to 11.08%) with respect to traditional metrics such as Average and Final Displacement Errors. A novel metric that stands independent of the number of samples is also proposed as a more fitting comparison for future works.