Human-Robot Interactions
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ItemDevelopment of a Highly Precise Place Recognition Module for Effective Human-robot Interactions in Changing Lighting and Viewpoint Conditions( 2020-01-07)We present a highly precise and robust module for indoor place recognition, extending the work by Lemaignan et al. and Robert Jr. by giving the robot the ability to recognize its environment context. We developed a full end-to-end convolutional neural network architecture, using a pre-trained deep convolutional neural network and the explicit inductive bias transfer learning strategy. Experimental results based on the York University and Rzeszów University dataset show excellent performance values (over 94.75 and 97.95 percent accuracy) and a high level of robustness over changes in camera viewpoint and lighting conditions, outperforming current benchmarks. Furthermore, our architecture is 82.46 percent smaller than the current benchmark, making our module suitable for embedding into mobile robots and easily adoptable to other datasets without the need for heavy adjustments.
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ItemInvestigating the Effect of Trust Manipulations on Affect over Time in Human-Human versus Human-Robot Interactions( 2020-01-07)The current study explored the influence of trust and distrust behaviors on affect over time. We examined the differences in affect when participants (N=97) were paired with a human or a robot while playing amodified version of the investorgame. Results indicated that there were no differences in affect between partner types when the partner performed a trustful behavior. When the partner performed a distrustful behavior, positive affect was higher for human partners than for robot partners. When robot partners performed a distrustful behavior, negative affect had a steeper incline compared to human partners. These findings suggest that people are more sensitive to distrust behaviors that are performed by a robot over a human.
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ItemTrusting a Humanoid Robot : Exploring Personality and Trusting Effects in a Human-Robot Partnership( 2020-01-07)Research on trust between humans and machines has primarily investigated factors relating to environmental or system characteristics, largely neglecting individual differences that play an important role in human behavior and cognition. This study examines the role of the Big Five personality traits on trust in a partnership between a human user and a humanoid robot. A wizard of oz methodology was used in an experiment to simulate an artificially intelligent robot that could be leveraged as a partner to complete a life or death survival simulation. Eye-tracking was employed to measure system utilization and validated psychometric instruments were used to measure trust and personality traits. Results suggest that individuals scoring high on the openness personality trait may have greater trust in a humanoid robot partner than those with low scores in the openness personality dimension.
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ItemHuman-Machine Interaction and Human Resource Management Perspective for Collaborative Robotics Implementation and Adoption( 2020-01-07)The shift towards human-robot collaboration (HRC) has the potential to increase productivity and sustainability, while reducing costs for the manufacturing industries. Indeed, it holds great potential for workplaces, allowing individuals to forsake repetitive or physically demanding jobs to focus on safer and more fulfilling ones. Still, integration of humans and machines in organizations presents great challenges to IS scholars due to the complexity of aligning digitalization and human resources. A knowledge gap does persist about organizational implications when it comes to implement collaborative robotics in the workplace and to support proper HRC. Thus, this paper aims to identify recommended human resources management (HRM) practices from previous research about human-robot interaction (HRI). As our results highlight that few studies attempted to fill the gap, a conceptual framework is proposed. It integrates HRM practices, technology adoption dimensions and main determinants of HRC, in the objective to support collaborative robotics implementation in organizations.
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