The performance of humanoid robots is improving, owing in part to their participation in robot games such as the DARPA Robotics Challenge. Along with the 2018 Winter Olympics in Pyeongchang, a Skiing Robot Competition was held in which humanoid robots participated autonomously in a giant slalom alpine skiing competition. The robots were required to transit through many red or blue gates on the ski slope to reach the finish line. The course was relatively short at 100 m long and had an intermediate-level rating. A 1.23 m tall humanoid ski robot, ‘DIANA’, was developed for this skiing competition. As a humanoid robot that mimics humans, the goal was to descend the slope as fast as possible, so the robot was developed to perform a carved turn motion. The carved turn was difficult to balance compared to other turn methods. Therefore, ZMP control, which could secure the posture stability of the biped robot, was applied. Since skiing takes place outdoors, it was necessary to ensure recognition of the flags in various weather conditions. This was ensured using deep learning-based vision recognition. Thus, the performance of the humanoid robot DIANA was established using the carved turn in an experiment on an actual ski slope. The ultimate vision for humanoid robots is for them to naturally blend into human society and provide necessary services to people. Previously, there was no way for a full-sized humanoid robot to move on a snowy mountain. In this study, a humanoid robot that transcends this limitation was realized.

As the importance of emotional interaction between humans and robots continues to gain attention, numerous studies have been conducted to identify the characteristics and effects of emotional HRI (Human–Robot Interaction) elements applied to robots. However, no study has yet combined various HRI elements into a single robot and conducted large-scale user experiments to determine which HRI element users prefer the most. This study selected four characteristics that facilitate attachment and emotional bonding between humans and animals: grooming, emotional transfer, imprinting, and cooperative hunting (play). These four characteristics were incorporated into the design and behavioral patterns of the robot EDIE as HRI elements. To allow users to effectively experience these elements, a 30 min runtime robot performance content featuring EDIE as the main character was developed. This large-scale experiment in the form of a performance enabled participants to engage with all four HRI elements and then respond to a survey identifying their most preferred element. Over two experiments involving a total of 3760 participants, this study examined trends in user preferences regarding the robot’s characteristics. By identifying the most effective HRI elements for fostering user attachment to robots, the findings aim to contribute to the harmonious coexistence of humans and robots.

As the importance of emotional interaction between humans and robots continues to gain attention, numerous studies have been conducted to identify the characteristics and effects of emotional HRI (Human-Robot Interaction) elements applied to robots. However, no study has yet combined various HRI elements into a single robot and conducted large-scale user experiments to determine which HRI element users prefer the most. This study selected four characteristics that facilitate attachment and emotional bonding between humans and animals: grooming, emotional transfer, imprinting, and cooperative hunting (play). These four characteristics were incorporated into the design and behavioral patterns of the robot EDIE as HRI elements. To allow users to effectively experience these elements, a 30-minute runtime robot performance content featuring EDIE as the main character was developed. This large-scale experiment in the form of a performance enabled participants to engage with all four HRI elements and then respond to a survey identifying their most preferred element. Over two experiments involving a total of 3,760 participants, this study examined trends in user preferences regarding the robot's characteristics. By identifying the most effective HRI elements for fostering user attachment to robots, the findings aim to contribute to the harmonious coexistence of humans and robots.