Original Research ARTICLE
Learning Semantics of Gestural Instructions for Human-Robot Collaboration
- 1University of Innsbruck, Austria
Designed to work safely alongside humans, collaborative robots need to be capable partners in human-robot teams. Besides having key capabilities like detecting gestures, recognizing objects, grasping them, and handing them over, these robots need to seamlessly adapt their behaviour for efficient human-robot collaboration. In this context we present the fast, supervised Proactive Incremental Learning (PIL) framework for learning associations between human hand gestures and the intended robotic manipulation actions. With the proactive aspect, the robot is competent to predict the human’s intent and perform an action without waiting for an instruction. The incremental aspect enables the robot to learn associations on the fly while performing a task. It is a probabilistic, statistically-driven approach. As a proof of concept, we focus on a table assembly task where the robot assists its human partner. We investigate how the accuracy of gesture detection affects the number of interactions required to complete the task. We also conducted a human-robot interaction study with non-roboticist users comparing a proactive with a reactive robot that waits for instructions.
Keywords: Human-robot collaboration, Proactive learning, Gesture understanding, Intention prediction, user study
Received: 04 Sep 2017;
Accepted: 02 Feb 2018.
Edited by:Malte Schilling, Bielefeld University, Germany
Reviewed by:Vadim Tikhanoff, Fondazione Istituto Italiano di Technologia, Italy
Mohan Sridharan, University of Auckland, New Zealand
Copyright: © 2018 Shukla, Erkent and Piater. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mr. Dadhichi Shukla, University of Innsbruck, Innsbruck, Austria, email@example.com