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Noris B.,Learning Algorithms and Systems Laboratory
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference | Year: 2011

In this paper, we report on a study on gaze behavior by children with Autism Spectrum Disorder (ASD) during a dyadic interaction in a naturalistic environment. Twelve children with ASD were contrasted to twelve typically developing (TD) children, in a semi-structured interaction with a selection of items from the Early Social Communication Scale (ESCS). We used the WearCam, a novel head-mounted eye-tracker designed for children, to obtain gaze information across the broad field of view from the viewpoint of the child. Children with ASD looked downwards more often, and explored their lateral field of view more extensively compared to TD children. We discuss a number of hypotheses in support of these observations. Source


Murphy R.R.,Texas A&M University | Nomura T.,Ryukoku University | Nomura T.,Advanced Technology and Research Intelligent Robotics and Communication Laboratories | Billard A.,Learning Algorithms and Systems Laboratory | Burke J.L.,SA Technologies, Inc.
IEEE Robotics and Automation Magazine | Year: 2010

The discussion and findings from the 'Teaching Humans About Human-Robot Interaction (HRI)' workshop, held at the IEEE/Robotics Society of Japan International Conference on Intelligent Robots and Systems (IROS), September 22, 2008, Nice, France, are presented. The workshop focused on low-cost robot kits made from servomotors and plastic water bottles and the resulting robot could take many configurations such as legs and snake structures and more emotive shapes similar to puppets. The discussions covered perfect syllabus or sequence of lectures for an HRI course and determined the perfect set of assignments and projects. The prerequisites for an HRI course depend on the target audience and scope of material, although probability and statistics was considered a universal prerequisite. The workshop found that a course development on robotics should consider industry needs, instructor constraints, and student learning preferences, as not all students will become HRI researchers. Source


De Souza R.,Learning Algorithms and Systems Laboratory | De Souza R.,University of Lisbon | El-Khoury S.,Learning Algorithms and Systems Laboratory | Santos-Victor J.,University of Lisbon | Billard A.,Learning Algorithms and Systems Laboratory
Robotics and Autonomous Systems | Year: 2015

In human grasping, choices are made on the use of hand-parts even before a grasp is realized. The human associates these choices with end-functionality and is confident that the resulting grasp will be able to meet task requirements. We refer to these choices on the use of hand-parts underlying grasp formation as the grasp intention. Modeling the grasp intention offers a paradigm whereby decisions underlying grasp formation may be related to the functional properties of the realized grasp in terms of quantities which may be sensed/recognized or controlled. In this paper we model grasp intention as mix of oppositions between hand parts. Sub-parts of the hand acting in opposition to each other are viewed as a basis from which grasps are formed. We compute a set of such possible oppositions and determine the most likely combination from the raw information present in a demonstrated grasp. An intermediate representation of raw sensor data exposes interactions between elementary grasping surfaces. From this, the most likely combination of oppositions is inferred. Grasping experiments with humans show that the proposed approach is robust enough to correctly capture the intention in demonstrated grasps across a wide range of hand functionality. © 2015 Elsevier B.V. Source

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