Realistic and robust head-eye coordination of conversational robot actors in human tracking applications

Rajruangrabin, Jartuwat; Popa, Dan O.

doi:10.1145/1579114.1579115

Cited by 2 publications

(6 citation statements)

References 7 publications

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“…And in [10], the dynamical characteristics of the head-eye motion are further factored into the distribution scheme via the addition of an angular velocity term into an objective function to minimize. This optimization has a similar effect to a quick eye movement (saccadic movement) during the transient response period, and to a minimal eye displacement as the object motion approach steady-state.…”

Section: Optimization Approach For Realistic Neck-eye Motion Distribumentioning

confidence: 99%

“…The solution to (10) can be used with equation (8) to compute the appropriate rotation angles of the eyes:…”

Section: Optimization Approach For Realistic Neck-eye Motion Distribumentioning

confidence: 99%

“…Furthermore, the solution for equation (10) can be obtained numerically, for instance using MATLAB optimization toolbox simplex search.…”

Section: Optimization Approach For Realistic Neck-eye Motion Distribumentioning

confidence: 99%

“…Social robots and human robot interaction has emerged as a research area in robotics in recent years [4,5,10,12,14]. One of the reasons behind this is the rapid improvements in sensor, actuator and processing capabilities in modern hardware.…”

Section: Introductionmentioning

confidence: 99%

“…The work presented in this paper is to build on prior work on human robot interaction [10] to create a framework that assists therapists by using robotic systems including the social robot RoboKind TM Zeno. Zeno is an emotional robot with expressive skin created by David Hanson, which appears to bridge Mori's uncanny valley.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced therapeutic interactivity using social robot Zeno

Ranatunga

Rajruangrabin

Popa

et al. 2011

Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments

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Social robotics has emerged as a new research area in recent years. One of the reasons behind this emergence is the rapid pace of improvements in sensor, actuator and processing capabilities in modern hardware enabling robots to interact with humans more effectively than ever before. The motivation for the work presented in this paper is to use advanced human-robot head-eye interaction algorithms in order to create a robotic framework that assists physical therapists treating sensor-motor impairments, such as Autism and Cerebral Palsy by using robotic systems. The robotic platform used in our work is the social robot Zeno, which has a fantastically friendly appearance and bridges the previously reported uncanny valley. In this paper we report on a new coordination algorithm based on reinforcement learning implemented on Zeno for achieving human like head-eye coordination to visually engage patients with cognitive impairments. The experimental results show that the various methods implemented enables social robot Zeno achieve natural head-eye coordination with significant improvement in accuracy without the need of extensive kinematic analysis of the system.

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Section: Optimization Approach For Realistic Neck-eye Motion Distribumentioning

confidence: 99%

“…The solution to (10) can be used with equation (8) to compute the appropriate rotation angles of the eyes:…”

Section: Optimization Approach For Realistic Neck-eye Motion Distribumentioning

confidence: 99%

“…Furthermore, the solution for equation (10) can be obtained numerically, for instance using MATLAB optimization toolbox simplex search.…”

Section: Optimization Approach For Realistic Neck-eye Motion Distribumentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Enhanced therapeutic interactivity using social robot Zeno

Ranatunga

Rajruangrabin

Popa

et al. 2011

Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments

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BioMove: Biometric User Identification from Human Kinesiological Movements for Virtual Reality Systems

Olade

Fleming

2020

Sensors

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Virtual reality (VR) has advanced rapidly and is used for many entertainment and business purposes. The need for secure, transparent and non-intrusive identification mechanisms is important to facilitate users’ safe participation and secure experience. People are kinesiologically unique, having individual behavioral and movement characteristics, which can be leveraged and used in security sensitive VR applications to compensate for users’ inability to detect potential observational attackers in the physical world. Additionally, such method of identification using a user’s kinesiological data is valuable in common scenarios where multiple users simultaneously participate in a VR environment. In this paper, we present a user study (n = 15) where our participants performed a series of controlled tasks that require physical movements (such as grabbing, rotating and dropping) that could be decomposed into unique kinesiological patterns while we monitored and captured their hand, head and eye gaze data within the VR environment. We present an analysis of the data and show that these data can be used as a biometric discriminant of high confidence using machine learning classification methods such as kNN or SVM, thereby adding a layer of security in terms of identification or dynamically adapting the VR environment to the users’ preferences. We also performed a whitebox penetration testing with 12 attackers, some of whom were physically similar to the participants. We could obtain an average identification confidence value of 0.98 from the actual participants’ test data after the initial study and also a trained model classification accuracy of 98.6%. Penetration testing indicated all attackers resulted in confidence values of less than 50% (<50%), although physically similar attackers had higher confidence values. These findings can help the design and development of secure VR systems.

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Realistic and robust head-eye coordination of conversational robot actors in human tracking applications

Cited by 2 publications

References 7 publications

Enhanced therapeutic interactivity using social robot Zeno

Enhanced therapeutic interactivity using social robot Zeno

BioMove: Biometric User Identification from Human Kinesiological Movements for Virtual Reality Systems

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