Robotic teaching assistants (TAs) often use body-mounted screens to deliver content. In nomadic, walk-and-talk learning, such as tours in makerspaces, these screens can distract learners from real-world objects, increasing extraneous cognitive load. HCI research lacks empirical comparisons of potential alternatives, such as robots with in-situ projection versus screen-based counterparts; little knowledge has been derived for designing such alternatives. We introduce ProjecTA, a semi-humanoid, gesture-capable TA that guides learners while projecting near-object overlays coordinated with speech and gestures. In a mixed-method study (N=24) in a university makerspace, ProjecTA significantly reduced extraneous load and outperformed its screen-based counterpart in perceived usability, usefulness of visual display, and cross-modal complementarity. Qualitative analyses revealed how ProjecTA’s coordinated projections, gestures and speech anchored explanations in place and time, enhancing understanding in ways a screen could not. We derive key design implications for future robotic TAs leveraging spatial projection to support mobile learning in physical environments.

To adapt to specific places and people, robots must recognize objects, which are typically taught by users-a tedious process.Inspired by anecdotes of positive teaching experiences shared by educators, sports coaches, and animal trainers, we developed seven experience-driven ways to make teaching a robot more engaging.For example, one interaction involved the robot prompting users to tell personal stories about the objects.A video vignette study (N=184) showed that experience-driven teaching was perceived as more positive than the current technology-driven teaching.Participants reported feeling more competent, connected to the robot, and valued.Additionally, the robot was perceived as more extroverted, open, agreeable, and conscientious.Overall, the experience-driven design of teaching interactions enhanced engagement and persistence by fostering reciprocal exchange and mutual understanding.In addition, the study lends support to an anecdotal approach to designing positive experiences through technology.

There seems to be a solid object inside.""What other toppings do you usually like on your pizza?" Figure 1: We imbued a robot with curious behaviors.The figure shows two examples.Left: The robot shakes a container to check whether there is an object inside.Right: The robot asks for the person's preferences.

Like humans, today's systems, such as robots and voice assistants, can express curiosity to learn and engage with their surroundings.While curiosity is a well-established human trait that enhances social connections and drives learning, no existing scales assess the perceived curiosity of systems.Thus, we introduce the Perceived System Curiosity (PSC) scale to determine how users perceive curious systems.We followed a standardized process of developing and validating scales, resulting in a validated 12-item scale with 3 individual sub-scales measuring explorative, investigative, and social dimensions of system curiosity.In total, we generated 831 items based on literature and recruited 414 participants for item selection and 320 additional participants for scale validation.Our results show that the PSC scale has inter-item reliability and convergent and construct validity.Thus, this scale provides an instrument to explore how perceived curiosity influences interactions with technical systems systematically.

Figure 1: The two-phase process for eliciting and verifying gestures.

Human-human handovers are natural and seamless. To be able to do this, humans optimize towards many factors. One of them is the timing when receiving an object. However, the preferred robot reaction time in Human-Robot handovers is currently unclear. To understand the preferred robot reaction time, we trained an Space-Time-Separable Graph Convolutional Network (STS-GCN) model using motion capture data of human-human handovers. We deployed this system on a robotic arm with live depth camera data. We conducted a user study (N=20) with five robot reaction times. We found that users perceived an early prediction as preferred. Furthermore, we found that designers can adapt this timing to their needs based on six sub-components of user perception. We contribute a ready-to-deploy hand over classification model, a preferred handover time for our system, and an approach to determine the preferred robot reaction time for robotic systems.

The gaze of a person tends to reflect their interest. This work explores what happens when this statement is taken literally and applied to robots. Here we present a robot system that employs a moving robot head with a screen-based eye model that can direct the robot’s gaze to points in physical space and present a reflection-like mirror image of the attended region on top of each eye. We conducted a user study with 33 participants, who were asked to instruct the robot to perform pick-and-place tasks, monitor the robot’s task execution, and interrupt it in case of erroneous actions. Despite a deliberate lack of instructions about the role of the eyes and a very brief system exposure, participants felt more aware about the robot’s information processing, detected erroneous actions earlier, and rated the user experience higher when eye-based mirroring was enabled compared to non-reflective eyes. These results suggest a beneficial and intuitive utilization of the introduced method in cooperative human-robot interaction.

In the pursuit of fully autonomous robotic systems capable of taking over tasks traditionally performed by humans, the complexity of open-world environments poses a considerable challenge. Addressing this imperative, this study contributes to the field of Large Language Models (LLMs) applied to task and motion planning for robots. We propose a system architecture that orchestrates a seamless interplay between multiple cognitive levels, encompassing reasoning, planning, and motion generation. At its core lies a novel replanning strategy that handles physically grounded, logical, and semantic errors in the generated plans. We demonstrate the efficacy of the proposed feedback architecture, particularly its impact on executability, correctness, and time complexity via empirical evaluation in the context of a simulation and two intricate real-world scenarios: blocks world, barman and pizza preparation.

How can a robot provide unobtrusive physical support within a group of humans? We present Attentive Support, a novel interaction concept for robots to support a group of humans. It combines scene perception, dialogue acquisition, situation understanding, and behavior generation with the common-sense reasoning capabilities of Large Language Models (LLMs). In addition to following user instructions, Attentive Support is capable of deciding when and how to support the humans, and when to remain silent to not disturb the group. With a diverse set of scenarios, we show and evaluate the robot’s attentive behavior, which supports and helps the humans when required, while not disturbing if no help is needed.

It seems that autonomous driving systems are substituting human responsibilities in the driving task. However, this does not mean that vehicles should not interact with their driver anymore, even in case of full automation. One reason is that the automation is not yet advanced enough to predict other road user's behavior in complex situations, which can lead to sub-optimal action choices, decrease comfort and user experience. In contrast, a human driver may have a more reliable understanding of other road users’ intentions which could complement that of the automation. We propose a framework that distinguishes between four levels for interaction with automation. Based on the framework, we introduce a concept which allows drivers to provide prediction-level guidance to an automated driving system through gaze-speech interaction. Results of a pilot user study show that people hold a positive attitude towards prediction-level intervention as well as the gaze-based interaction method.

Driving is a social activity: Drivers need to coordinate and cooperate with each other to share the infrastructure. The relationship between drivers influences their driving behavior and experience. Lights, horn and speed are the most frequently used means to exchange information, limiting both the range and the bandwidth of the connectivity and leading to isolation, loneliness, and competition. We present “iSticker” and “MusicHound”, two concepts that aim to establish a connection by presenting similarity information between drivers. The two concepts were prototyped and evaluated with users in a driving simulator. The results showed that iSticker and MusicHound enhance drivers’ social closeness with each other and belongingness during the journey.

The road environment can be seen as a social situation: Drivers need to coordinate with each other to share the infrastructure. In addition to the driving behaviour itself, lights, horn and speed are the most frequently used means to exchange information, limiting both the range and the bandwidth of the connectivity and leading to misunderstanding and conflict. With everywhere available connectivity and the broad penetration of social network services, the relationship between drivers on the road may gain more transparency, enabling social information to pass through the steel shell of the cars and giving opportunities to reduce misunderstanding and strengthen empathy. In this study, we present "CarNote", a concept that aims to reduce misunderstanding and conflict between drivers by showing their emergency driving status to others. This concept was prototyped and evaluated with users in a driving simulator. The results showed that CarNote enhances drivers' empathy, increases forgiveness and decreases anger to others on the road.

Driving is a social activity, and therefore driving is not only a matter of skills but also of emotion. Numerous studies show that aggressive driving makes a significant contribution to traffic accident involvement. In previous research, a concept based on Driver to Driver communication employing location-based services was proposed that enables road users to express their disapproval and appreciation of others' driving behavior. In the current study, a complete prototype based on this concept, which enables participants to send and receive feedback while driving and review their behavior afterwards, was developed. The acceptance and influence of driving behaviour of this concept were investigated in a driving simulator. It was found that the system positively influenced people's driving behavior and was accepted by most participants.