The role of early anticipations for human-robot ball catching

Abstract

Intention inference from observation of human actions is an essential ability for robots performing interactive tasks. This paper studies the role of early anticipation skills to improve the performance of a robotic system playing ball catching with a human partner. The source of anticipatory information results from the observation of the thrower's motion before the ball is released. For that purpose, a feed-forward neural network is trained to estimate the initial position and velocity of the ball in-flight given a sequence of observations during the throwing phase. The proposed approach outperforms up to 20% the classical methodology in which the generation of predictions solely relies upon the available information during the flight phase. Several simulation results demonstrate the added value of early anticipation skills from the viewpoint of ball catching performance.