United States

A critical issue during HRI is eliciting an intentional stance in the human interactor, who then perceive the agent as an &quot;intelligent&quot; being with full agency towards their own intentions and desires. Eliciting such a stance, however, has proven elusive. We argue for a paradigm shift in our approach to this problem, based on recent evidence from social robotics and digital avatars. To trigger an intentional stance in humans, perhaps our artificial agents need to adopt one about themselves.

IEEE RO-MAN 2021

What if it speaks like it was from the village? Effects of a Robot speaking in Regional Language Variations on Users’ Evaluations

autonomous agents

social cognition

human robot interaction

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
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A ticket is required to attend this event, please register using the link below:

https://aamas2022-conference.auckland.ac.nz/attending/registration/

Registration Is Required

AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

A critical issue during HRI is eliciting an intentional stance in the human interactor, who then perceive the agent as an "intelligent" being with full agency towards their own intentions and desires. Eliciting such a stance, however, has proven elusive. We argue for a paradigm shift in our approach to this problem, based on recent evidence from social robotics and digital avatars. To trigger an intentional stance in humans, perhaps our artificial agents need to adopt one about themselves.

technical paper

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. This theme also touches upon people's expectations and perceptions of robots, as well as other social, cognitive and cultural factors that will influence human-robot interactions. We will have a special session/track on this topic, invite targeted submissions and include plenary speakers who are specialist in this area. We sincerely hope that this focus will help enlighten the design-thinking of all of us as we fabricate and program our own various systems.

To access RO-MAN Virtual 2021, [please register here.](https://ro-man2021.org/registration/)

RO-MAN 2021's theme is trustworthiness and cooperation. We chose this term well before the pandemic put the world on edge and added a new reason for us to question our own personal safety when in the company of others. But what about in the company of social robots? What would cause us to trust, or not trust, them? How do we as researchers create that feeling, that critical component of any relationship? With social robots, it is of course largely in our power to design the physical form, the control code and the interface software to maximize trustworthiness, facilitate cooperation and enhance the quality of human-robot interaction. 

We try to design a control law using MPC and predictive tracking of a person using LSTM for a socially adept person following robot. The human-robot shared autonomy is the motivation. Tracking a person when it goes out of sight is a challenging task, for which LSTMs are brought to the picture. This adds the natural human part to the work.

Towards Out-Of-Sight Predictive Tracking for Long-Term Indoor Navigation of Non-Holonomic Person Following Robot

When we humans interact with each other, we adapt our behaviors to the people we interact with. We do this by interpreting subtle cues and signals from them in each interaction. With this paper, we created a robot that uses speech and body movement characteristics to recognize and distinguish between users interacting with it through small duration interactions. We investigate how these characterics are influenced by changing the physical context.

A Minimalistic Approach to User Group Adaptation of Robot Behaviors Using Movement and Speech Analysis

This research has developed a new method for predicting natural human arm movements. It introduces the five-parameter logistic function into this application space and uses supervised learning to predict fingertip trajectories and elbow swivel angles. The method presents small error in an experimental study, improving upon previous methods and verifying its effectiveness.

Human Arm Motion Prediction in Reaching Movements

We present a 3D human activity simulator, ``HOIsim", focusing on Human-Object Interactions (HOIs). Using HOIsim we model two complex activities for ``lunch" and ``breakfast". Furthermore, we introduce methods to meaningfully randomize activity flows and the environment topology, and make a dataset of the 3D pose maps publicly available.

HOIsim: Synthesizing Realistic 3D Human-Object Interaction Data for Human Activity Recognition

This work presents a method for recognition of walking activities and prediction of gait periods using wearable sensors. First, a Convolutional Neural Network (CNN) is used to recognise the walking activity and gait period. Second, the output of the CNN is used by a Predicted Information Gain (PIG) method to predict the next most probable gait period while walking. The output of these two processes are combined to adapt the recognition accuracy of the system. This adaptive combination allows us to achieve an optimal recognition accuracy over time.

Parents’ Views on Using Social Robots for Language Learning

Gesture are a natural modality of human interaction. Smart technologies are seamlessly part of our daily lives, becoming essential to endow them with gesture recognition capbilities to ensure a natural interaction. This work leverages the power of convolutional neural networks proposinga novel spatiotemporal visually interppretable image encoding representation of skeleton data for CNN-based gesture recognition.

Robotic Object Sorting via Deep Reinforcement Learning: a Generalized Approach

In this research, we investigate the use of External Human Machine Interface for delivery robots for expressing their navigational intent. Through a survey, we explore the effectiveness of display and light-based external interfaces in expressing the delivery robots navigational intent.

Understanding robot acceptance/rejection: the SAR model

Social robots have strong potential in health and social care, but the embodiment of these devices is important to ensure acceptability and adoption. Based on a reflection over four studies, we provide insight into the impact of various morphology types. Our results suggest the combination of familiar, zoomorphic appearance with animacy, life-simulation and speech capabilities was an interesting area of research for future robots developed for health and social care.

Morphology of Socially Assistive Robots for Health and Social Care: A Reflection on 24 Months of Research with Anthropomorphic, Zoomorphic and Mechanomorphic Devices

Can a verbal message by a robot change your attitude? More importantly, can it worsen your attitude? Our research study highlights how speech-based appeals
by a robot can successfully influence individuals to trust it with a valuable asset, i.e. their mental health. However, while an emotional appeal by a robot is more effectual
on affective attitudes, it backfires significantly more than a logical appeal when used on cognitive attitudes, leading to trust dissolution. It follows that a robot can, in fact,
successfully arouse emotions in order to persuade. In theory, robots may have an upper hand in executing an effective persuasion attempt compared to humans, who inherently use emotions when they have the intention to persuade.

Would You Trust a Robot with Your Mental Health? The Interaction of Emotion and Logic in Persuasive Backfiring

In this paper, we conduct a full meta-analysis of the existing literature on personality similarity versus dissimilarity (matching/mismatching) and attempt to come to a consensus on if robots with matching personalities are accepted more readily than those with mismatching personalities. In addition, we provide several gaps in this literature worth pursuing.

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Towards Out-Of-Sight Predictive Tracking for Long-Term Indoor Navigation of Non-Holonomic Person Following Robot

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Next from IEEE RO-MAN 2021

Towards Out-Of-Sight Predictive Tracking for Long-Term Indoor Navigation of Non-Holonomic Person Following Robot

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Environment Guided Interactive Reinforcement Learning: Learning from Binary Feedback in High-Dimensional Robot Task Environments

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