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Editorial Introduction to ACM THRI Volume 8, Issue 2

A Diagnostic Human Workload Assessment Algorithm for Collaborative and Supervisory Human--Robot Teams

High-stress environments, such as first-response or a NASA control room, require optimal task... (more)

Modeling of Human Visual Attention in Multiparty Open-World Dialogues

This study proposes, develops, and evaluates methods for modeling the eye-gaze direction and head orientation of a person in multiparty open-world... (more)

Age Difference in Perceived Ease of Use, Curiosity, and Implicit Negative Attitude toward Robots

Understanding older adults’ attitudes toward robots has become increasingly important as... (more)

What Do Older Adults and Clinicians Think About Traditional Mobility Aids and Exoskeleton Technology?

Mobility impairments can prevent older adults from performing their daily activities, which highly... (more)

Engaging with Robotic Swarms: Commands from Expressive Motion

In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted... (more)

Animation Techniques in Human-Robot Interaction User Studies: A Systematic Literature Review

There are many different ways a robot can move in Human-Robot Interaction. One way is to use techniques from film animation to instruct the robot to... (more)

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Special issue on Representation Learning for Human and Robot Cognition

Intelligent robots are rapidly moving to the center of human environment; they collaborate with human users in different applications that require high-level cognitive functions so as to allow them to understand and learn from human behavior within different Human-Robot Interaction (HRI) contexts. Read more here.

Forthcoming Articles
Using event representations to generate robot semantics

The key idea of this article is to use events as the fundamental structures for the semantic representations of a robot. Events are modeled in terms of conceptual spaces and mappings between spaces. It is shown how the semantics of major word classes can be described with the aid of conceptual spaces in a way that is amenable to computer implementations. An event is represented by two vectors, one force vector representing an action and one result vector representing the effect of the action. The two-vector model is then extended with the thematic roles so that an event is built up from an agent, an action, a patient, and a result. It is shown how the components of an event can be put together to semantic structures that represent the meanings of sentences.

When Exceptions are the Norm

HRI researchers have made major strides in developing robotic architectures that are capable of reading a limited set of social cues and producing behaviors that enhance their likeability and feeling of comfort amongst humans. However, the cues in these models are fairly direct and the interactions largely dyadic. To capture the normative qualities of interaction more robustly, we propose consent as a distinct, critical area for HRI research. Convening important insights in existing HRI work around topics like touch, proxemics, gaze, and moral norms, the notion of consent reveals key expectations that can shape how a robot acts in social space. By sorting various kinds of consent through social and legal doctrine, we delineate empirical and technical questions to meet consent challenges faced in major application domains and robotic roles. Attention to consent could show, for example, how extraordinary, norm-violating actions can be justified by agents and accepted by those around them. We argue that operationalizing ideas from legal scholarship can better guide how robotic systems might cultivate and sustain proper forms of consent.

An Ontology for Human-Human Interactions and Learning Interaction Behavior Policies

Robots are expected to possess similar capabilities that humans exhibit during close proximity dyadic interaction. Humans can easily adapt to each other in a multitude of scenarios, ensuring safe and natural interaction. Even though there have been attempts to mimic human motions for robot control, understanding the motion patterns emerging during dyadic interaction has been neglected. In this work, we analyze close proximity human-human interaction and derive an ontology that describes a broad range of possible interaction scenarios by abstracting tasks, and using insights from attention theory. This ontology enables us to group interaction behaviors into separate cases, each of which can be represented by a graph. Using inverse reinforcement learning, we find unique interaction models, represented as combination of cost functions, for each case. The ontology offers a unified, and generic approach to categorically analyze and learn close proximity interaction behaviors that can enhance natural human-robot collaboration.

Neural-network-based memory for a social robot: Learning a memory model of human behavior from data

Many recent studies have shown that behaviors and interaction logic for social robots can be learned automatically from natural examples of human-human interaction by machine learning algorithms, with minimal input from human designers. Oftentimes in human-human interactions, a person?s action is dependent upon actions that occurred much earlier in the interaction. However, in previous approaches to data-driven social robot behavior learning, the robot decides its next action based only on a narrow window of the interaction history. In this work, an analysis of the types of memory-setting and memory-dependent actions that occur in a camera shop interaction scenario are presented. Furthermore, a robot behavior system with a recurrent neural network architecture with gated recurrent units is proposed to tackle the problem of learning relevant memories to enable a shopkeeper robot to perform appropriate memory-dependent actions. In an offline evaluation the proposed system performed appropriate memory-dependent actions at a significantly higher rate than a baseline system without memory. Finally, an analysis of how the recurrent neural network with gated recurrent units learned to represent memories is presented.

Curiosity did not kill the robot: A curiosity-based learning system for a shopkeeper robot

Bridging multilevel time scales in HRI: An analysis framework

this paper, we present a multi-level time scales framework for analysis of human-robot interaction (HRI). Such framework allows HRI scientists to model the inter-relation between measures and factors of an experiment. Our final goal with introducing this framework is to unify scientific practice in HRI community for better reproduciblility. Our new approach transposes Newell's framework of human actions to model human-robot interaction. Measures from the interaction are sorted in categories (time scales) corresponding to the temporal constraints proposed by Newell. According to this sorting, a bottom-up or top-bottom analysis can then be performed to correlate variables that allows to better explain the interaction. Utilization of this method within a child-robot interaction involving 2 robots and one child playing a memory game is then presented to illustrate the potential and use of this method. Finally we introduce clear guidelines to re-use the framework.

Beyond R2D2: Designing multimodal interaction behavior for robot-specific morphology

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