Human-robot Interaction Techniques Research Articles

A Review on Human-Robot Interaction and User Experience in Smart Robotic Wheelchairs

This review research paper provides an analysis of the current state of Human-Robot Interaction (HRI) and User Experience (UX) in the context of smart robotic wheelchairs. It explores the advancements in HRI techniques, including multimodal interfaces, gesture recognition, voice commands, and brain-computer interfaces, and evaluates their impact on user experience factors such as usability, learnability, efficiency, and satisfaction. The paper discusses the role of artificial intelligence and machine learning in enhancing HRI capabilities and personalization of wheelchair behavior. The review highlights gaps in current research and identifies future directions to improve the immersive experience of smart wheelchair users. Overall, this comprehensive review contributes to a deeper understanding of the factors influencing user acceptance, satisfaction, and system performance, guiding the development of more intuitive and user-centered smart robotic wheelchairs for individuals with mobility impairments.

Estimation of perturbations in robotic behavior using dynamic mode decomposition

Physical human–robot interaction tasks require robots that can detect and react to external perturbations caused by the human partner. In this contribution, we present a machine learning approach for detecting, estimating, and compensating for such external perturbations using only input from standard sensors. This machine learning approach makes use of Dynamic Mode Decomposition (DMD), a data processing technique developed in the field of fluid dynamics, which is applied to robotics for the first time. DMD is able to isolate the dynamics of a nonlinear system and is therefore well suited for separating noise from regular oscillations in sensor readings during cyclic robot movements. In a training phase, a DMD model for behavior-specific parameter configurations is learned. During task execution, the robot must estimate the external forces exerted by a human interaction partner. We compare the DMD-based approach to other interpolation schemes. A variant, sparsity promoting DMD, is particularly well suited for high-noise sensors. Results of a user study show that our DMD-based machine learning approach can be used to design physical human–robot interaction techniques that not only result in robust robot behavior but also enjoy a high usability.

Emotional Interaction with a Robot Using Facial Expressions, Face Pose and Hand Gestures

Facial expression is one of the major cues for emotional communications between humans and robots. In this paper, we present emotional human robot interaction techniques using facial expressions combined with an exploration of other useful concepts, such as face pose and hand gesture. For the efficient recognition of facial expressions, it is important to understand the positions of facial feature points. To do this, our technique estimates the 3D positions of each feature point by constructing 3D face models fitted on the user. To construct the 3D face models, we first construct an Active Appearance Model (AAM) for variations of the facial expression. Next, we estimate depth information at each feature point from frontal- and side-view images. By combining the estimated depth information with AAM, the 3D face model is fitted on the user according to the various 3D transformations of each feature point. Self-occlusions due to the 3D pose variation are also processed by the region weighting function on the normalized face at each frame. The recognized facial expressions - such as happiness, sadness, fear and anger - are used to change the colours of foreground and background objects in the robot displays, as well as other robot responses. The proposed method displays desirable results in viewing comics with the entertainment robots in our experiments.

Special Issue on Human Robot Interaction

Recent advances in robotics are disseminating robots into the social living environment as humanoids, pets, and caregivers. Novel human-robot interaction techniques and interfaces must be developed, however, to ensure that such robots interact as expected in daily life and work. Unlike conventional personal computers, such robots may assume a variety of configurations, such as industrial, wheel-based, ambulatory, remotely operated, autonomous, and wearable. They may also implement different communications modalities, including voice, video, haptics, and gestures. All of these aspects require that research on human-robot interaction become interdisciplinary, combining research from such fields as robotics, ergonomics, computer science and, psychology. In the field of computer science, new directions in human-computer interaction are emerging as post graphical user interfaces (GUIs). These include wearable, ubiquitous, and real-world computing. Such advances are thereby bridging the gap between robotics and computer science. The open-ended problems that potentially face include the following: What is the most desirable type of interaction between human beings and robots? What sort of technology will enable these interactions? How will human beings accept robots in their daily life and work? We are certain that readers of this special issue will be able to find many of the answers and become open to future directions concerning these problems. Any information that readers find herein will be a great pleasure to its editors.