The design space of human-robot interaction is large and multi-dimensional. A sound design requires a systematic theory-driven exploration, specification and refinement of design variables. There is a need for a practical method and tool to iteratively specify the content of the dialogue (e.g., speech acts) with the accompanying expressive behavior (e.g., gesture openness) as prescribed by social science theory, e.g., task- and person-oriented communication. This paper presents an iterative interaction-design (ID) method for multi-modal robot communication. Following the ID-method, a designer first creates his/her own individual design and, subsequently, provides an iteration to the evolving iterative design. To support the design method, we developed an ID-tool (available for download). The tool support entails (a) selecting the theory-based communication style; (b) creating and linking the dialogue act components for the concerning use case; and (c) setting the associated expression parameters. We conducted a study with Industrial Design students (N=13) who followed the ID-method and used our tool to design person- and task-oriented communications for a reception robot. Our method produced distinctive task- and person-oriented dialogue styles, i.e., provided the predicted theory-based multi-modal communicative behaviors. The task-oriented style showed a more formal, shorter and less chatty communication. Overall, there was a rather smooth design convergence process, in which the individual designs were harmonized into the iterative design. For the selected design problem, the ID-tool had a satisfactory usability. Next steps include validation of the communication styles in an empirical study and, subsequently, identification of reusable design patterns.@en

This paper presents the design and evaluation of human-like welcoming behaviors for a humanoid robot to draw the attention of passersby by following a three-step model: (1) selecting a target (person) to engage, (2) executing behaviors to draw the target's attention, and (3) monitoring the attentive response. A computer vision algorithm was developed to select the person, start the behaviors and monitor the response automatically. To vary the robot's enthusiasm when engaging passersby, a waving gesture was designed as basic welcoming behavioral element, which could be successively combined with an utterance and an approach movement. This way, three levels of enthusiasm were implemented: Mild (waving), moderate (waving and utterance) and high (waving, utterance and approach movement). The three levels of welcoming behaviors were tested with a Pepper robot at the entrance of a university building. We recorded data and observation sheets from several hundreds of passersby (N = 364) and conducted post-interviews with randomly selected passersby (N = 28). The level selection was done at random for each participant. The passersby indicated that they appreciated the robot at the entrance and clearly recognized its role as a welcoming robot. In addition, the robot proved to draw more attention when showing high enthusiasm (i.e., more welcoming behaviors), particularly for female passersby.@en

Humans use several social cues, both verbal and nonverbal, to draw the attention of others. In this study we investigate whether similar behaviors can also be effectively used by a social robot for drawing attention. To this end, we setup a welcoming humanoid (Pepper) at the entrance of a university building. Its behaviors include one or a combination of behavioral modalities (i.e., a waving gesture, utterance and movement). These behaviors are triggered automatically based on people detection software which tracks passersby and monitors their head keypoints. Our findings imply that Pepper draws more attention when displaying a combination of modalities.

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Drawing the attention of passersby is a basic task of a social robot to initiate an interaction in a public environment (e.g., shopping malls, museums or hospitals). Humans use several social cues, both verbal and nonverbal, to draw the attention of others. In this study, we investigate whether similar behaviors can also be effectively used by a social robot for drawing attention. To this end, we setup a humanoid robot (Pepper) to act as a welcoming robot at the entrance of a university building. The behaviors selected for Pepper include one or a combination of behavioral modalities (i.e., a waving gesture, utterance and movement). These behaviors are triggered automatically using the output of people detection software which tracks passersby and monitors their head keypoints (nose, eyes, and ears). The reactions of people toward Pepper are observed and recorded by means of an observation sheet. For several weeks, we deployed Pepper at the entrance with the aim of wearing off the novelty effect. In our final study, we collected data from several hundreds of passersby N=364 and conducted post-interviews with randomly selected ones N=28. Passersby noticed Pepper at the entrance and clearly recognized its role as a welcoming robot. In addition, Pepper was able to draw more attention when displaying a combination of behavioral modalities. However, passersby did not recall the robot utterance as they, for example, were unable to reproduce it or mistakenly claimed that the robot said something when it was only waving.

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We report Year 4 progress in the TRADR project WP5: Persistent models for human-robot teaming. We focused on the analysis, modelling and online-processing of the information-gathering tasks that the human-robot team is performing during a mission, with the goal to enable the robotic system to follow the mission (understand which tasks have been assigned to whom, what the progress is) and provide support for the management of the activities through the agent system and based on the working agreements. The reported work includes further development of team communication processing, ontology modelling, task management support, working agreements. The developed modules are integrated in the TRADR system and were evaluated during the TRADR evaluation exercise.@en

Task allocation and management is crucial for human-robot collaboration in Urban Search And Rescue response efforts. The job of a mission team leader in managing tasks becomes complicated when adding multiple and different types of robots to the team. Therefore, to effectively accomplish mission objectives, shared situation awareness and task management support are essential. In this paper, we design and evaluate an ontology which provides a common vocabulary between team members, both humans and robots. The ontology is used for facilitating data sharing and mission execution, and providing the required automated task management support. Relevant domain entities, tasks, and their relationships are modeled in an ontology
based on vocabulary commonly used by firemen, and a user interface is designed to provide task tracking and monitoring. The ontology design and interface are deployed in a search and rescue system and its use is evaluated by firemen in a task allocation and management scenario. Results provide support that the proposed ontology (1) facilitates information sharing during missions; (2) assists the team leader in task allocation and management; and (3) provides automated support for managing an Urban Search and Rescue mission.@en

Visualizing and simulating the real world by means of three-dimensional (3D) models is important in many fields, especially in science, technology, engineering and medicine (STEM). Exploiting the human senses, such as the sense of sight with head-mounted displays (HMDs) and the sense of touch with haptic devices, has helped in creating immersive virtual-reality (VR) experiences. However, HMDs and haptics have seldom been combined, and recently the technology has been advancing rapidly in both areas. The objective of this research was to develop a VR system which combines both a consumer-level HMD and a mid-level haptic device using a game-development platform. A proof-of-concept system was developed using the Oculus Rift HMD and the Phantom Premium 1.5 High Force haptic device. The system was implemented using the Unity 3D game engine and was tested with two 3D human anatomical models, a heart and part of a skull. The technical performance of the system was evaluated, and a small preliminary user evaluation was performed. Particular challenges and limitations of currently available hardware and software are also discussed@en