ACM Transactions on

Human-Robot Interaction (THRI)

Latest Articles

The impact of intergroup bias on trust and approach behaviour towards a humanoid robot

Understanding agency in interactions between children with autism and socially assistive robots

A new UGV teleoperation interface for improved awareness of network connectivity and physical surroundings

How should a robot approach two people?

Touching a mechanical body


In January 2018, the Journal of Human-Robot Interaction (JHRI) became an ACM publication and has been rebranded as the ACM Transactions on Human-Robot Interaction (THRI). It will continue to be open access, fostering the widest possible readership of HRI research and information. All issues will be available in the ACM Digital Library. Read more here.


Special issue on Artificial Intelligence for Human-Robot Interaction (AI-HRI)

Developing such systems often involves innovations and integrations between many deep and diverse technical areas, including but not limited to task and motion planning, learning from demonstration, dialogue synthesis, activity recognition and prediction, human behavior modeling, and shared control. For this special issue we are soliciting high quality, original articles that present the design and/or evaluation of novel computational techniques and systems at the intersection of artificial intelligence and human-robot interaction research. We aim to bring together a wide variety of articles to showcase the state-of-the-art in AI-HRI within a single issue of the world's leading journal of Human-Robot Interaction research. Read more here.

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. To this end, a stubborn challenge that attracts much attention in artificial intelligence is representation learning, which refers to learning representations of data so as to efficiently extract relevant features for probabilistic, nonprobabilistic, or connectionist classifiers. This active area of research spans different fields and applications including speech recognition, object recognition, emotion recognition, natural language processing, language emergence and development, in addition to mirroring different human cognitive processes through appropriate computational modeling.  Read more here.

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