Artificial Trust as a Tool in Human-AI Teams

Mutual trust is considered a required coordinating mechanism for achieving effective teamwork in human teams. However, it is still a challenge to implement such mechanisms in teams composed by both humans and AI (human-AI teams), even though those are becoming increasingly prevalent. Agents in such teams should not only be trustworthy and...

Towards a Real-time Measure of the Perception of Anthropomorphism in Human-robot Interaction

How human-like do conversational robots need to look to enable long-term human-robot conversation? One essential aspect of long-term interaction is a human's ability to adapt to the varying degrees of a conversational partner's engagement and emotions. Prosodically, this can be achieved through (dis)entrainment. While speech-synthesis has...

A Study on Automated Receptionists in a Real-World Scenario

The commercial availability of robots and voice-operated smart devices such as Alexa or Google Home have some companies wondering whether they can replace some current human interactions by using these devices. One such area of interaction is at the reception desk. While both platforms can offer the necessary interaction features to take on...

Emotion in reinforcement learning agents and robots: A survey

This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents. The survey focuses on agent/robot emotions, and mostly ignores human user emotions. Emotions are recognized as functional in decision-making by influencing motivation and action selection. Therefore, computational emotion models are...

Viewpoint optimization for aiding grasp synthesis algorithms using reinforcement learning

Grasp synthesis for unknown objects is a challenging problem as the algorithms are expected to cope with missing object shape information. This missing information is a function of the vision sensor viewpoint. The majority of the grasp synthesis algorithms in literature synthesize a grasp by using one single image of the target object and...

Active vision via extremum seeking for robots in unstructured environments: Applications in object recognition and manipulation

In this paper, a novel active vision strategy is proposed for optimizing the viewpoint of a robot's vision sensor for a given success criterion. The strategy is based on extremum seeking control (ESC), which introduces two main advantages: 1) Our approach is model free: It does not require an explicit objective function or any other task...

Coactive Design: Designing Support for Interdependence in Joint Activity

Coactive Design is a new approach to address the increasingly sophisticated roles that people and robots play as the use of robots expands into new, complex domains. The approach is motivated by the desire for robots to perform less like teleoperated tools or independent automatons and more like interdependent teammates. In this article, we...