Feeling together and at the same time feeling free while sharing the same roof is a balance that newly retired couples try to reach. Indeed the beginning of retirement is complex, and sometimes, even when both spouses find themselves at home together, some spouses could experience a feeling of loneliness. To respond to this insight, we introduce the concept of “sensible presence robject” - Yōkobo to fill this loneliness gap through subtle interactions. The pictorial introduces and describes the different steps of the design process of Yōkobo as a non-anthropomorphic and non-vocal robot for the entrance of dwellings. Through its expressiveness, Yōkobo is a presence messenger for newly retired couples. On a larger scale, this research is a manifesto for the slow technology trend in which perceptions and time open a discussion on poetic sensibility.

This article presents a novel software architecture enabling the analysis of assembly actions from fine-grained hand motions. Unlike previous works that compel humans to wear ad-hoc devices or visual markers in the human body, our approach enables users to move without additional burdens. Modules developed are able to: (i) reconstruct the 3D motions of body and hands keypoints using multi-camera systems; (ii) recognize objects manipulated by humans, and (iii) analyze the relationship between the human motions and the manipulated objects. We implement different solutions based on OpenPose and Mediapipe for body and hand keypoint detection. Additionally, we discuss the suitability of these solutions for enabling real-time data processing. We also propose a novel method using Long Short-Term Memory (LSTM) deep neural networks to analyze the relationship between the detected human motions and manipulated objects. Experimental validations show the superiority of the proposed approach against previous works based on Hidden Markov Models (HMMs).

Human motion retargeting to humanoid robots (i.e., transferring motion data to robots for human imitation) is a challenging process with many potential real-work applications. However, current state-of-the-art frameworks present practical limitations, such as the requirement of camera calibration and the implementation of expensive equipment for motion capture. Therefore, we propose a novel framework for motion retargeting based on a single-view camera and human pose estimation. Unlike previous works, our framework is cost and computationally efficient, and it is applicable both on prerecorded uncalibrated videos and web-camera live streams. The framework is composed of three modules: 1) 2D coordinate extraction from the integrated Google BlazePose, 2) 3D modeling by depth estimation using a geometrical algorithm, and 3) human joint angles computation and input process to Pepper robot. Pepper’s imitation accuracy is evaluated qualitatively by direct motion similarity observation and quantitatively by comparison between output and input motion data to observe the effect of Pepper’s physical limitations. Results suggest that our proposed framework is able to reproduce human-like motion sequences, however with some limitations due to the hardware.

Studies have shown that the appearance and movements of home robots may play key roles in the impression and engagement of users, opposed to recent rises in the smart speaker market. In this research, we conduct a user experiment with the aim of clarifying the elements required to evaluate the human impression of a robot's movements, based on the hypothesis that adequate movements may lead to better impressions and engagement. We compare the impressions of participants who interacted with a robot with movements (behavior robot) and a robot without movements (non-behavior robot). Results show that when using the behavior robot, participants showed significantly higher values in their impressions of cheerfulness and sociability. Questionnaires about interaction revealed that personalization is also an important function for robots to make a good impression on humans.

In this paper we introduce a gesture-based robot control framework, we discuss the adopted design principles and we report results about its evaluation with humans. Gesture-based control using wearable devices may constitute a novel form of human-robot interaction, but its implications have not been discussed in the literature. We discuss the main challenging issues, possible design guidelines and an open source, freely available implementation using commercially available devices and robots. The overall performance of the architecture, as well as its validation with 27 untrained volunteers, is reported.