Robot assistance selection for large object manipulation with a human
Résumé
In this paper, we propose a method that allows a human to perform complex manipulation tasks jointly with a robotic partner. To that end, the robot has a library of assistances that it can provide for helping the human partner during a priori unknown collaborative tasks. According to the haptic cues naturally transmitted by the human partner, the robot selects on-line the suitable assistance for the current intended collaborative motion. Based on the naive bayes classifier and the Matthew Correlation Coefficient, the parameters of the decisionmaking are automatically tuned. An experiment on a real arm manipulator is provided to validate the proposed approach.