The aging population and chronic staff shortages are prompting care facilities to use service robots (SR) as part of daily care. There are high hopes for support with physical workloads and routine tasks, but adoption often stalls due to technical complexity, poor integration into workflows, and fears that "support" could become "replacement". We address this problem by viewing care as a value-driven practice rather than a list of tasks. In a participatory interview study with caregivers and care recipients in three facilities, based on value-sensitive design, we identified expectations, non-negotiable boundaries, and the values that should guide robot behavior. Participants identified credible roles for SRs in logistics, documentation, reminders, and guidance, but rejected intimate or safety-critical care tasks. Acceptance depends on value-oriented and fluid adaptivity. Robots should dynamically modulate initiative, proactivity, and interaction modality to maintain human attentiveness and warmth, sustain independence, support control over workload, and take legal safeguards into account. We contribute to this (1) with an empirically grounded overview of acceptable potentials and limitations guided by stakeholder values, (2) with a value-sensitive design-based framework for fluid adaptivity as a mechanism that operationalizes values in daily interaction, and (3) design requirements for user-centered, transparent, and context-sensitive SRs that reduce workload and create space for human care rather than replacing it.

We present a multi-modal stress dataset that uses digital job interviews to induce stress. The dataset provides multi-modal data of 40 participants including audio, video (motion capturing, facial landmarks, eye tracking), as well as physiological information (photoplethysmography, electrodermal activity). In addition to that, the dataset contains time-continuous annotations for stress and occurred emotions (e.g., shame, anger, anxiety, and surprise). In order to establish a baseline, five different machine learning classifiers (Support Vector Machine, K-Nearest Neighbors, Random Forest, Feed-forward Neural Network, and Long-Short-Term Memory Network) have been trained and evaluated on the presented dataset for a binary stress classification task. The best-performing classifier has been a Long-Short-Term Memory Network, which achieved an accuracy of 91.7% and an F1-score of 90.2%. The ForDigitStress dataset is freely available to other researchers.

Robotic technology has proven to be advantageous for student learning and social development in educational settings. However, in order to enhance their effectiveness and provide a more human-like tutoring experience, robots must be capable of adapting to the user and exhibiting proactivity. By acting proactively, these intelligent robotic tutors can anticipate potential obstacles and take preventative measures to avoid negative outcomes. However, determining when and how to behave proactively remains an open question. This study investigates how a robotic tutor can utilize a student’s cognitive-affective states to trigger proactive tutoring dialogue and improve the learning experience. Specifically, we observed a concept learning task scenario where a robotic assistant proactively assisted the user when negative states, such as frustration and confusion, were detected. In an empirical study involving 40 undergraduate and doctoral students, we evaluated whether the initiation of proactive behavior after the detection of signs of confusion and frustration improves the student’s concentration and trust in the robot. We also examined which level of proactive dialogue is most effective for promoting concentration and trust. The results indicate that high levels of proactive behavior can harm trust, especially when triggered during negative cognitive-affective states. However, this behavior does contribute to keeping the student focused on the task when triggered during these states. Based on our findings, we discuss potential future steps for improving the proactive assistance of robotic tutoring systems.

The next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive. Well-defined proactive behavior may improve human-machine cooperation, as the agent takes a more active role during interaction and takes off responsibility from the user. However, proactivity is a double-edged sword because poorly executed pre-emptive actions may have a devastating effect on the task outcome and the relationship with the user. For designing adequate proactive dialog strategies, we propose a novel approach including both social and task-relevant features in the dialog. Here, the primary goal is to optimize proactive behavior so that it is task-oriented - this implies high task success and efficiency - while also being socially effective by fostering user trust. Including both aspects in the reward function for training a proactive dialog agent using reinforcement learning showed the benefit of our approach for more successful human-machine cooperation.

In this work, we present a robot-dialogue framework to handle sophisticated robot-initiated interaction. We introduce a robotic assistant equipped with a dialogue system in a household assistance context. To become a truly collaborative companion, the assistant is able to engage in a proactive conversation for task assistance. The system actions are triggered by the recognition of persons or specific objects. To evaluate our system, we conducted a user study with 17 participants in a laboratory environment where users were able to interact with the system via natural language. The results showed that the behaviour of the system was accepted and perceived as trustworthy by the users.

Robots will eventually enter our daily lives and assist with a variety of tasks. Especially in the household domain, robots may become indispensable helpers by overtaking tedious tasks, e.g. keeping the place tidy. Their effectiveness and efficiency, however, depend on their ability to adapt to our needs, routines, and personal characteristics. Otherwise, they may not be accepted and trusted in our private domain. For enabling adaptation, the interaction between a human and a robot needs to be personalized. Therefore, the robot needs to collect personal information from the user. However, it is unclear how such sensitive data can be collected in an understandable way without losing a user’s trust in the system. In this paper, we present a conversational approach for explicitly collecting personal user information using natural dialogue. For creating a sound interactive personalization, we have developed an empathy-augmented dialogue strategy. In an online study, the empathy-augmented strategy was compared to a baseline dialogue strategy for interactive personalization. We have found the empathy-augmented strategy to perform notably friendlier. Overall, using dialogue for interactive personalization has generally shown positive user reception.

Trust forms an important factor in human-robot interaction and is highly influencing the success or failure of a mixed team of humans and machines. Similarly, to human-human teamwork, communication and proactivity are one of the keys to task success and efficiency. However, the level of proactive robot behaviour needs to be adapted to a dynamically changing social environment. Otherwise, it may be perceived as counterproductive and the robot’s assistance may not be accepted. For this reason, this work investigates the design of a socially-adaptive proactive dialogue strategy and its effects on humans’ trust and acceptance towards the robot. The strategy is implemented in a human-like household assistance robot that helps in the execution of domestic tasks, such as tidying up or fetch-and-carry tasks. For evaluation of the strategy, users interact with the robot while watching interactive videos of the robots in six different task scenarios. Here, the adaptive proactive behaviour of the robot is compared to four different levels of static proactivity: None, Notification, Suggestion, and Intervention. The results show that proactive robot behaviour that adapts to the social expectations of a user has a significant effect on the perceived trust in the system. Here, it is shown that a robot expressing socially-adaptive proactivity is perceived as more competent and reliable than a non-adaptive robot. Based on these results, important implications for the design of future robotic assistants at home are described.

In mixed-initiative user interactions, a user and an autonomous agent collaborate for solving tasks by taking interleaving actions. However, this shift of control towards the agent requires a formation of trust for the user, otherwise the assistance possibly will be rejected and becomes obsolete. One approach for fostering a trustworthy interaction is to equip an agent with proactive dialogue capabilities. However, the development of adequate proactive dialogue strategies is complex and highly user- as well as context-dependent. Inappropriate usage of proactive conversation may even do more harm than good and corrupt the human-computer trust relationship. In order to alleviate this problem, modelling and predicting a proactive system’s perceived trustworthiness during an ongoing interaction is essential. Therefore, this paper presents novel work on the development of a user model for live prediction of trust during proactive interaction, incorporating user-, system-, and context-dependent features. For predicting trust, three machine-learning algorithms – support vector machine, eXtreme Gradient Boost, gated recurrent unit network – are trained and tested on a proactive dialogue corpus. The experimental results show that among the classifiers the support vector machine showed the most well-rounded performance, while the gated recurrent unit had the best accuracy. The results prove the developed user model to be reliable for predicting trust in proactive dialogue. Based on the outcomes, the usability of the proposed method in real-life scenarios is discussed and implications for developing user-adaptive proactive dialogue strategies are described.

Effectively supporting novices during performance of complex tasks, e.g. do-it-yourself (DIY) projects, requires intelligent assistants to be more than mere instructors. In order to be accepted as a competent and trustworthy cooperation partner, they need to be able to actively participate in the project and engage in helpful conversations with users when assistance is necessary. Therefore, a new proactive version of the DIY-assistant Robert is presented in this paper. It extends the previous prototype by including the capability to initiate reflective meta-dialogues using multimodal cues. Two different strategies for reflective dialogue are implemented: A progress-based strategy initiates a reflective dialogue about previous experience with the assistance for encouraging the self-appraisal of the user. An activity-based strategy is applied for providing timely, task-dependent support. Therefore, user activities with a connected drill driver are tracked that trigger dialogues in order to reflect on the current task and to prevent task failure. An experimental study comparing the proactive assistant against the baseline version shows that proactive meta-dialogue is able to build user trust significantly better than a solely reactive system. Besides, the results provide interesting insights for the development of proactive dialogue assistants.

Recommendation systems aim at facilitating information retrieval for users by taking into account their preferences. Based on previous user behaviour, such a system suggests items or provides information that a user might like or find useful. Nonetheless, how to provide suggestions is still an open question. Depending on the way a recommendation is communicated influences the user’s perception of the system. This paper presents an empirical study on the effects of proactive dialogue strategies on user acceptance. Therefore, an explicit strategy based on user preferences provided directly by the user, and an implicit proactive strategy, using autonomously gathered information, are compared. The results show that proactive dialogue systems significantly affect the perception of human-computer interaction. Although no significant differences are found between implicit and explicit strategies, proactivity significantly influences the user experience compared to reactive system behaviour. The study contributes new insights to the human-agent interaction and the voice user interface design. Furthermore, we discover interesting tendencies that motivate futurework.

Intelligent computer systems aim at providing user-assistance for challenging tasks, like decision-making, planning, or learning. For offering optimal assistance, it is essential for such systems to decide when to be reactive or proactive and how active system behaviour should be designed. Especially, as this decision may greatly influence the user's trust in the system. Therefore, we conducted a mixed-factorial study which examines how different levels of proactivity (none, notification, suggestion, and intervention) as well as timing strategies (fixed-timing and insecurity-based) are trusted by subjects while performing a planning task. The results showed, that proactive system behaviour is perceived trustworthy in insecure situations independent of the timing. However, proactive dialogue showed strong effects on cognition-based trust (system's perceived competence and reliability) depending on task difficulty. Furthermore, fully autonomous system behaviour fails to establish an adequate human-computer trust relationship, in contrast to conservative strategies.

Novice users require assistance when performing handicraft tasks. Adequate instruction ensures task completion and conveys knowledge and abilities required to perform the task. We present an assistant teaching novice users how to operate electronic tools, such as drills, saws, and sanders, in the context of Do-It-Yourself (DIY) home improvement projects. First, the actions that need to be performed for the project are determined by a planner. Second, a dialogue manager capable of natural language interaction presents these actions as instructions to the user. Third, questions on these actions and involved objects are answered by generating appropriate ontology-based explanations.

Adapting Spoken Dialogue Systems to the user is supposed to result in more efficient and successful dialogues.In this work, we present an evaluation of a quality-adaptive strategy with a user simulator adapting the dialogue initiative dynamically during the ongoing interaction and show that it outperforms conventional non-adaptive strategies and a random strategy.Furthermore, we indicate a correlation between Interaction Quality and dialogue completion rate, task success rate, and average dialogue length.Finally, we analyze the correlation between task success and interaction quality in more detail identifying the usefulness of interaction quality for modelling the reward of reinforcement learning strategy optimization.