Robots such as those serving as educational tutors, healthcare supporters, and collaborative partners must develop “rapport,” a construct that encompasses mutual understanding and interpersonal connection with people, to ensure their long-term success. In our earlier work, we constructed, evaluated, and validated an 18-item Connection–Coordination Rapport (CCR) scale to measure human–robot rapport (Studies 1–3). Even though the full-length 18-item CCR scale measures rapport thoroughly, it may not always be practical for researchers to adopt given its relatively long length. Therefore, in this work, we developed a reduced-length version of the CCR scale that still effectively measures rapport using just 8 items. Following recommended practices for short-form development and validation, we leveraged the input of Human–Robot Interaction (HRI) experts (Study 4, \(N=30\) ) to shorten the CCR scale from 18 items to 8 items (4 items per factor). Then, we evaluated this reduced-length CCR scale on a new sample (Study 5, \(N=186\) ) where online participants watched a HRI video and evaluated it using both the full-length and reduced-length CCR scales. We validated the reduced-length CCR scale by showing that it has high internal reliability, high overlap with the full-length CCR scale, a consistent factor structure, high construct validity, and significant time savings.

Robots, particularly in service and companionship roles, must develop positive relationships with people they interact with regularly to be successful. These positive human-robot relationships can be characterized as establishing “rapport,” which indicates mutual understanding and interpersonal connection that form the groundwork for successful long-term human-robot interaction. However, the human-robot interaction research literature lacks scale instruments to assess human-robot rapport in a variety of situations. In this work, we developed the 18-item Connection-Coordination Rapport (CCR) Scale to measure human-robot rapport. We first ran Study 1 (N = 288) where online participants rated videos of human-robot interactions using a set of candidate items. Our Study 1 results showed the discovery of two factors in our scale, which we named “Connection” and “Coordination.” We then evaluated this scale by running Study 2 (N = 201) where online participants rated a new set of human-robot interaction videos with our scale and an existing rapport scale from virtual agents research for comparison. We also validated our scale by replicating a prior in-person human-robot interaction study, Study 3 (N = 44), and found that rapport is rated significantly greater when participants interacted with a responsive robot (responsive condition) as opposed to an unresponsive robot (unresponsive condition). Results from these studies demonstrate high reliability and validity for the CCR scale, which can be used to measure rapport in both first-person and third-person perspectives. We encourage the adoption of this scale in future studies to measure rapport in a variety of human-robot interactions.

Live entertainment is moving towards a greater participatory culture, with dynamic narratives told through audience interaction. Robot characters offer a unique opportunity to mitigate the challenges of creating personalized entertainment at scale. However, robots often cannot react to audience responses, limiting opportunities for audience participation. In this work, we explore methods to increase user agency in live entertainment experiences with robot characters to improve user engagement and enjoyment. In a between-subjects study (N=60), we create an immersive story where users role-play as detectives with two distinct robot characters. Users either (1) have greater involvement and self-identification in the story by talking with the robots in-character (narrative condition), (2) have a more active role in solving puzzles (gameplay condition), or (3) follow along without being prompted by the robots for input (control condition). Our results show that increasing user agency in a role-playing experience, in either its narrative or its gameplay, improves users' flow state, sense of autonomy and competence, verbal engagement, and perceptions of the robot characters' engagement. Increasing narrative agency also led to longer unprompted reactions from participants, while gameplay agency improved feelings of immersion and relatedness with the robots. These findings suggest that creating either narrative or gameplay agency can improve user engagement, which can extend to broader robot interactions where gameplay elements and role-playing in stories can be incorporated.

We introduce ThrowIO, a novel style of actuated tangible user interface that facilitates throwing and catching spatial interaction powered by mobile wheeled robots on overhanging surfaces. In our approach, users throw and stick objects that are embedded with magnets to an overhanging ferromagnetic surface where wheeled robots can move and drop them at desired locations, allowing users to catch them. The thrown objects are tracked with an RGBD camera system to perform closed-loop robotic manipulations. By computationally facilitating throwing and catching interaction, our approach can be applied in many applications including kinesthetic learning, gaming, immersive haptic experience, ceiling storage, and communication. We demonstrate the applications with a proof-of-concept system enabled by wheeled robots, ceiling hardware design, and software control. Overall, ThrowIO opens up novel spatial, dynamic, and tangible interaction for users via overhanging robots, which has great potential to be integrated into our everyday space.

Despite the clear benefits that social connection offers to well-being, strangers in close physical proximity regularly ignore each other due to their tendency to underestimate the positive consequences of social connection. In a between-subjects study (N = 49 pairs, 98 participants), we investigated the effectiveness of a humanoid robot, a computer screen, and a poster at stimulating meaningful, face-to-face conversations between two strangers by posing progressively deeper questions. We found that the humanoid robot facilitator was able to elicit the greatest compliance with the deep conversation questions. Additionally, participants in conversations facilitated by either the humanoid robot or the computer screen reported greater happiness and connection to their conversation partner than those in conversations facilitated by a poster. These results suggest that technology-enabled conversation facilitators can be useful in breaking the ice between strangers, ultimately helping them develop closer connections through face-to-face conversations and thereby enhance their overall well-being.

Robots are becoming increasingly prominent in the entertainment sphere, where they interact with guests in themed environments to tell stories, often in place of human characters. To evaluate the potential benefits of robots in these contexts compared to humans, we created an interactive puzzle game where either a robot or a human actor serves as a diegetic "game guide" character that is both a cooperative partner and an omniscient game master. In the game, participants solve a crime mystery by asking the game guide for information to complete tasks and for hints to solve puzzles. We conducted a between-subjects study (n = 42) to investigate how players’ game experiences differed when the game guide was a human compared to an embodied robot. Our results show that participants playing with a robot had more fun, felt less judged, and felt more connected with the robot while solving tasks compared to those playing with a human. These results suggest that robots can be effective alternatives to human actors in broader immersive entertainment contexts such as escape rooms to provide greater enjoyment and promote more social interaction with in-game characters.

The concept of the two-dimensional spectro-temporal modulation filtering of the auditory model is implemented for the FFT spectrogram. It analyzes the spectrogram in terms of the temporal dynamics and the spectral structures of the sound. The overlap and add (OTA) method, which is more convenient and reliable than the iterative-projection method proposed in, is used to invert the FFT spectrogram back to sounds. The Non-Negative Sparse Coding (NNSC) method is adopted to demonstrate the benefit of our analysis-synthesis procedures in a noise suppression application. Even without fine-tuning parameters, our proposed analysis-synthesis procedures offer benefits in de-noising especially under low SNR conditions.