Human Gaze and Focus-of-Attention in Dual Reality Human-Robot Collaboration

Moniri, Mohammad Mehdi; Valcárcel, Fabio Andrés Espinosa; Merkel, Dieter; Sonntag, Daniel

doi:10.1109/ie.2016.54

Cited by 32 publications

(12 citation statements)

References 9 publications

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“…These techniques have been successfully applied in HRI for children with Autism Spectrum Disorders (ASD) [100], [101]. Attention formulation could be achieved either by deictic words or vocal expressions such as "look here, see me, look right, or next one", by pointing gesture, by using line of sight, by using communication cue such as eye gazing [102], [103] or by combination of vocal and gesture commands [104], [105].…”

Section: A Joint Attention Formulationmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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This paper presents a state-of-the-art survey on robotic systems, sensors, actuators and collaborative strategies for Physical Human-Robot Collaboration (pHRC). The paper starts with an overview of some robotic systems with cuttingedge technologies (sensors and actuators) suitable for pHRC operations and the intelligent assist devices employed in pHRC. Sensors being among the essential components to establish communication between a human and a robotic system are surveyed. The sensor supplies the signal needed to drive the robotic actuators. The survey reveals that the design of new generation collaborative robots and other intelligent robotic systems has paved the way for sophisticated learning techniques and control algorithms to be deployed in pHRC. Furthermore, it revealed relevant components needed to be considered for effective pHRC to be accomplished. Finally, a discussion of the major advances made, some research directions, and future challenges are presented.

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Section: A Joint Attention Formulationmentioning

confidence: 99%

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Ogenyi

Liu

Yang

et al. 2021

IEEE Trans. Cybern.

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“…After setting up Microsoft Kinect (as an input for skeletal tracking of the user) and putting on Oculus Rift DK2 (as an output device for stereoscopic visual display, and as an input device for head motion tracking), the user can interact with the prototype in virtual reality environment. In [17], the author using Oculus Rift DK2 as visual display, and a tracking system that tracks the user's head position, pose, and eye-gaze, achieved controlling a real Baxter robot to pick up a part. And a simulated robot moving synchronously.…”

Section: Literature Review On Hrc With Virtual Realitymentioning

confidence: 99%

Human-Robot Collaboration: Task Sharing Through Virtual Reality

Shu

Sziebig

Pieskä

2018

IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society

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Collaborative Robots provide many possibilities, when it comes to Human-Robot Collaboration. Until now, these approaches are usually custom made, sensor-integrated solutions, where the robot's safety controller ensures the safety of the human worker. These solutions are according to today's rules and standards. We propose to extend these solutions with including Virtual Realty as a sensor and to provide comfort features to the operator. In order to create cooperation between human and industrial robot in our experiments, we propose to have a simple nut screwing operation as an example, where the industrial robot does the hard part. With sharing the task in such manner, we will ensure that the robot is doing the hard and monotonous work, while the worker benefits from the task sharing. Results are demonstrated through simulation and in reality also.

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“…To solve these problems, we apply user gaze information in the evaluation of candidate solutions in IEC. Gaze information includes the user's potential preferences, which are derived from various systems [11]. For applying user gaze information to the evaluation of candidate solutions, IEC systems can obtain user evaluation information by merely allowing users to view multiple candidate solutions.…”

Section: Introductionmentioning

confidence: 99%

Interactive Evolutionary Computation System with User Gaze Information

Takenouchi

Tokumaru

2019

IJAE

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We present an Interactive Evolutionary Computation (IEC) system that applies user gaze information. Historically, IEC systems have encountered the problems caused by heavy user evaluation loads. To solve these problems, researchers have employed biologically derived user information, such as heartbeats or brainwaves, to reduce the evaluation load. However, the requirement for users to wear special devices to measure this information has limited the popularity of these systems. Therefore, we applied the user gaze information approach to solve these problems. Gaze information includes the user's potential preferences, which are derived from various processes. When user gaze information is applied in the evaluation of candidate solutions, IEC systems can obtain user evaluation information while users are viewing multiple candidate solutions. In this paper, we verify the effectiveness of the eye tracking IEC system using evaluation experiments with real users. In the experiment, we use a normal IEC system as a comparison method where users manually evaluate candidate solutions using a 10-stage evaluation process. The experimental results show that the eye tracking IEC method can generate solutions with results equivalent to those of the compared system.

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Human Gaze and Focus-of-Attention in Dual Reality Human-Robot Collaboration

Cited by 32 publications

References 9 publications

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Physical Human–Robot Collaboration: Robotic Systems, Learning Methods, Collaborative Strategies, Sensors, and Actuators

Human-Robot Collaboration: Task Sharing Through Virtual Reality

Interactive Evolutionary Computation System with User Gaze Information

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