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AFFECT-HRI: A Comprehensive Human-Robot Interaction Dataset

Researchers introduced the AFFECT-HRI dataset, designed to address the lack of open data for developing emotion recognition capabilities in human-robot interaction (HRI). This dataset combines subjective human-affect assessments with objective physiological sensor data collected during interactions between participants and anthropomorphic service robots in a complex retail scenario.
Samudrapom Damapr Susha Cheriyedath Scientific Data Comprehensive Dataset Human Robot Interaction Comprehensive Human Robot

New Robot Uses Touch to Foster Social Engagement in Children

A group of researchers from Cornell University and the University of Lisbon recently unveiled a touch-based robot that can promote inclusion in classrooms by providing kids with and without visual impairments with equal opportunities to engage in group activities with the robot.
Isabel Neto International Conference On Human Gaips Lab Laboratory Of Robotics Cornell University University Of Lisbon

UC San Diego Roboticists Showcase Excellence at Human-Robot Interaction 2024 Conference

University of California, San Diego robotics research– from supporting patient home care and stroke rehabilitation to facilitating mission critical teamwork among first responders– was on display during the ACM/IEEE International Conference on Human-
United States San Diego Laurel Riek Department Of Emergency Medicine International Conference On Human Jacobs School Of Engineering

UC San Diego Roboticists Shine at Human Robot Interaction 2024 Conference

UC San Diego robotics research-- from supporting patient home care and stroke rehabilitation to facilitating mission critical teamwork among first responders-- was on display during the ACM/IEEE International Conference on Human-Robot Interaction (HRI).
United States San Diego Laurel Riek Uc San Diego Healthcare Robotics Lab University Of California Healthcare Robotics Lab
Source: ucsd.edu

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Engineering household robots to have a little common sense

MIT engineers aim to give robots a bit of common sense when faced with situations that push them off their trained path, so they can self-correct after missteps and carry on with their chores. The team’s method connects robot motion data with the common sense knowledge of large language models, or LLMs.
Yanwei Wang Michael Hagenow Julie Shah Jiayuan Mao Tsun Hsuan Wang Department Of Aeronautics
Source: mit.edu

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