Recurrent neural networks for inferring intentions in shared tasks for industrial collaborative robots

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Industrial robots are evolving to work closely with humans in shared spaces. Hence, robotic tasks are increasingly shared between humans and robots in collaborative settings. To enable a fluent human robot collaboration, robots need to predict and respond in real-time to worker's intentions. We present a method for early decision using force information. Forces are provided naturally by the user through the manipulation of a shared object in a collaborative task. The proposed algorithm uses a recurrent neural network to recognize operator's intentions. The algorithm is evaluated in terms of action recognition on a force dataset. It excels at detecting intentions when partial data is provided, enabling early detection and facilitating a quick robot reaction.This work is supported by the Regional Catalan Agency ACCIÓ through the RIS3CAT2016 project SIMBIOTS (COMRDI16-1-0017), the Spanish State Research Agency through the María de Maeztu Seal of Excellence to IRI (Insti-tut de Robòtica Informàtica Industrial) (MDM-2016-0656), the HuMoUR project TIN2017-90086-R (AEI/FEDER, UE), and the European Social Fund and the Ministry of Business and Knowledge of Catalonia through the FI 2020 grant.Peer ReviewedPostprint (author's final draft