This paper presents an implementation of RoSA, a Robot System Assistant, for safe and intuitive human-machine interaction. The interaction modalities were chosen and previously reviewed using a Wizard of Oz study emphasizing a strong propensity for speech and pointing gestures. Based on these findings, we design and implement a new multi-modal system for contactless human-machine interaction based on speech, facial, and gesture recognition. We evaluate our proposed system in an extensive study with multiple subjects to examine the user experience and interaction efficiency. It reports that our method achieves similar usability scores compared to the entirely human remote-controlled robot interaction in our Wizard of Oz study. Furthermore, our framework’s implementation is based on the Robot Operating System (ROS), allowing modularity and extendability for our multi-device and multi-user method.

The goal of the study was to research different communication modalities needed for intuitive Human-Robot Interaction. This study utilizes a Wizard of Oz prototyping method to enable a restriction-free, intuitive interaction with an industrial robot. The data from 36 test subjects suggests a high preference for speech input, automatic path planning and pointing gestures. The catalogue developed during this experiment contains intrinsic gestures suggesting that the two most popular gestures per action can be sufficient to cover the majority of users. The system scored an average of 74% in different user interface experience questionnaires, while containing forced flaws. These findings allow a future development of an intuitive Human-Robot interaction system with high user acceptance.

The ability to measure the engagement level of humans interacting with robots paves the way towards intuitive and safe human-robot interaction. Recent approaches achieve reasonable progress in predicting human engagement in physically situated environments. However, engagement estimation is still a challenging problem especially in an open-world environment due to the difficulty of creating and monitoring a variety of human social cues in real-time. Furthermore, the interactions may involve a group of subjects interacting simultaneously with the robot, which increases the prediction complexity. In this paper, we design a real-time engagement estimation system for humans interacting with robots with generalization capability. We propose to estimate engagement using a three-stage approach based on a combination of learning-based and rule-based approaches. Firstly, state-of-the-art deep learning methods are used to extract engagement features from input frames. Then, a simple neural network is used to estimate the focus of attention score by incorporating gaze and head pose features and assigning this score to all subjects in the scene using a face recognition algorithm. Finally, a rule-based classification approach is used to predict the engagement state of the subject to initiate/terminate the interaction with the robot. To effectively evaluate our system, we access our approach for each phase separately. Additionally, we use an online evaluation study in which subjects are allowed to interact freely with an industrial robot. Our model achieves an average of 96%, 90%, and 93% precision, recall, and F-score respectively.

Recently, face recognition became a key element in social cognition which is used in various applications including human–robot interaction (HRI), pedestrian identification, and surveillance systems. Deep convolutional neural networks (CNNs) have achieved notable progress in recognizing faces. However, achieving accurate and real-time face recognition is still a challenging problem, especially in unconstrained environments due to occlusion, lighting conditions, and the diversity in head poses. In this paper, we present a robust face recognition and tracking framework in unconstrained settings. We developed our framework based on lightweight CNNs for all face recognition stages, including face detection, alignment and feature extraction, to achieve higher accuracies in these challenging circumstances while maintaining the real-time capabilities required for HRI systems. To maintain the accuracy, a single-shot multi-level face localization in the wild (RetinaFace) is utilized for face detection, and additive angular margin loss (ArcFace) is employed for recognition. For further enhancement, we introduce a face tracking algorithm that combines the information from tracked faces with the recognized identity to use in the further frames. This tracking algorithm improves the overall processing time and accuracy. The proposed system performance is tested in real-time experiments applied in an HRI study. Our proposed framework achieves real-time capabilities with an average of 99%, 95%, and 97% precision, recall, and F-score respectively. In addition, we implemented our system as a modular ROS package that makes it straightforward for integration in different real-world HRI systems.

Intuitive and safe interfaces for robots are challenging issues in robotics. Robo-HUD is a gadget-less interaction concept for contactless operation of industrial systems. We use virtual collision detection based on time-of-flight sensor data, combined with augmented reality and audio feedback, allowing the operators to navigate a virtual menu by “hover and hold” gestures. When incorporated with virtual safety barriers, the collision detection also functions as a safety feature, slowing or stopping the robot if a barrier is breached. Additionally, a user focus recognition module monitors the awareness, enabling the interaction only when intended. Early case studies show that these features present good use-cases for inspection tasks and operation in difficult environments, where contactless operation is needed.

Multimodal user interfaces promise natural and intuitive human–machine interactions. However, is the extra effort for the development of a complex multisensor system justified, or can users also be satisfied with only one input modality? This study investigates interactions in an industrial weld inspection workstation. Three unimodal interfaces, including spatial interaction with buttons augmented on a workpiece or a worktable, and speech commands, were tested individually and in a multimodal combination. Within the unimodal conditions, users preferred the augmented worktable, but overall, the interindividual usage of all input technologies in the multimodal condition was ranked best. Our findings indicate that the implementation and the use of multiple input modalities is valuable and that it is difficult to predict the usability of individual input modalities for complex systems.