Social robots are increasingly applied as health behavior change interventions, yet actionable knowledge to guide their design and evaluation remains limited. This systematic review synthesizes (1) the behavior change strategies used in existing HRI studies employing social robots to promote health behavior change, and (2) the evaluation methods applied to assess behavior change outcomes. Relevant literature was identified through systematic database searches and hand searches. Analysis of 39 studies revealed four overarching categories of behavior change strategies: coaching strategies, counseling strategies, social influence strategies, and persuasion-enhancing strategies. These strategies highlight the unique affordances of social robots as behavior change interventions and offer valuable design heuristics. The review also identified key characteristics of current evaluation practices, including study designs, settings, durations, and outcome measures, on the basis of which we propose several directions for future HRI research.

Aviation electrical connectors are essential components in the aircraft electrical wiring interconnection system (EWIS), responsible for information and energy transmission. Even a minor fault in a connector pin can critically affect the reliability and stability of the EWIS. However, traditional faulty pin detection methods rely heavily on manual visual inspection, which is inefficient and susceptible to missed or false detections due to the inherent limitations of human observation. To address these challenges, this article introduces a novel system-level approach that transforms the defect detection process from the physical domain to a virtual one powered by digital twin (DT) technology. A general framework for DT-based defect detection is proposed and instantiated through the design and implementation of a DT-enabled automated faulty pin detection (DT-AFPD) system. The DT-AFPD system integrates 3-D machine vision into a complete detection pipeline encompassing equipment design, data acquisition, DT model construction, algorithm development, and system deployment. Specifically, a 4-degree-of-freedom (4-DOF) device equipped with a 3-D structured light camera is developed to acquire point cloud data of aviation connectors. Several preprocessing techniques are applied to reduce data volume and enhance point cloud quality. Based on this, a connector structure-aware faulty pin detection algorithm, named CSA-FPD, is designed to detect short and bent pins using limited data. The proposed DT-AFPD system is validated on 13 representative types of aviation electrical connectors, covering over 3600 pins. Experimental results demonstrate that the system achieves an average detection precision of 99.85%, effectively reducing the probability of EWIS reinstallation and enhancing the reliability of faulty pin detection.

This study investigates how interaction scenarios of human caregiving for robots affect humans’ perceived bond with robots. In a between-subjects lab experiment (n = 88), participants played a game with a social robot during which they provided either 1) emotional care (comforting the robot); 2) instrumental care (helping with battery charging); or 3) no care for the robot. Results indicated that caregiving did not significantly affect human-robot bonding according to explicit relationship measures including closeness, social attraction, or desire for future interaction. However, caregiving mattered when bonding was measured implicitly. Those in the emotional caregiving scenario were more hesitant to replace the robot and invested more effort in a voluntary task requested by the robot than those who provided no care. These findings provide empirical evidence that emotional caregiving interactions can effectively foster initial human-robot bonding, highlighting a promising design scenario for human-robot interaction.

Social robots hold significant potential to positively impact people's lives by persuading them to change their health behaviors. Yet, there is limited knowledge on how to effectively design these robots to achieve this goal. This PhD project investigates how persuasive communication strategies and human-robot relational bonds can enhance the influence of human-social robot interactions on health behavior change. This extended abstract outlines the motivation, problem statement, research questions, and research activities (including completed, ongoing, and planned studies) of this project, as well as its expected contributions to the HRI community.

To design social robots to effectively promote health behavior change, it is essential to understand how people respond to various health communication strategies employed by these robots. This study examines the effectiveness of two types of social control strategies from a social robot-relationship-focused strategies (emphasizing relational consequences) and target-focused strategies (emphasizing health consequences)-in encouraging people to reduce sedentary behavior. A two-session lab experiment was conducted (n = 135), where participants first played a game with a robot, followed by the robot persuading them to stand up and move using one of the strategies. Half of the participants joined a second session to have a repeated interaction with the robot. Results showed that relationship-focused strategies motivated participants to stay active longer. Repeated sessions did not strengthen participants' relationship with the robot, but those who felt more attached to the robot responded more actively to the target-focused strategies. These findings offer valuable insights for designing persuasive strategies for social robots in health communication contexts.

Trust plays a crucial role in decision-making during human-robot collaboration, particularly in emergency scenarios where it becomes more susceptible due to dynamic factors. Misalignment of human-robot trust significantly hampers the efficiency of the collaboration. Therefore, it is imperative to establish effective human-robot interaction and decision support mechanisms that mitigate biases in human confidence levels regarding the robot’s capabilities in dynamic environments. Additionally, online correction of human-robot trust based on human behavioral feedback is vital. This paper focuses on a specific type of emergency task scenario, specifically, indoor human-robot collaborative patrolling in the event of sudden power outages. We propose a trust model based on linear Gaussian and sparse Gaussian processes (sparse GP). We also employ Monte Carlo Tree Search (MCTS) method to determine the robot’s optimal fusion decision-making. Through VR-based human-robot collaborative experiments, we ascertain that the robot prioritizes enhancing human-robot trust in emergency scenarios to mitigate the long-term costs of human-robot collaboration.Note to Practitioners—The primary motivation of this paper lies in tackling the issue of decreased collaboration efficiency in human-robot cooperation, stemming from irrational human decision-making, a problem exacerbated in emergency scenarios where comprehending all available information proves challenging. Grounded in the realm of human-robot trust, this study evaluates the temporal and substantive aspects of human-robot interactions contingent upon the degree of trust. Moreover, it adapts the fusion decision-making process within the human-robot team in accordance with the decision-making strategies adopted by humans at varying trust levels. The optimized decisions of the human-robot ensemble are conveyed to the human participant via decision support. This approach ensures the maintenance of trust levels, facilitating the acceptance of decision support that may seem intuitively irrational but holds objective superiority. Furthermore, it mitigates redundant human-robot interactions once a sufficient level of trust is established.

Flight control system testing (FCST) is one of the most important process to check whether flight control surfaces can operate properly according to commands during aircraft assembly. Traditional testing method relies heavily on manual labor, leading to low efficiency and inconsistent quality. In this paper, we apply digital twin (DT) technology to the FCST process for the first time. We firstly design an architecture of DT-enabled FCST including four layers to support further development. Then, we present a triangular mesh alignment-based angle measurement (TMA-AM) algorithm to efficiently collect deflection angle data for DT-enabled FCST. Extensive experiments conducted on a aircraft wing subassembly platform show that the TMA-AM algorithm achieves an average angular measurement error of less than 0.1°, outperforming existing methods. Moreover, we develop a virtual experimental platform named DT-FCST aligned with a real aircraft wing subassembly platform. In addition, TMA-AM algorithm is integrated with the DT-FCST platform. By integrating real-time data from the cockpit, real-time physical-virtual interaction of aircraft control sticks and flight control surfaces are achieved, ensuring consistency between physical and virtual movements. The integration of DT technology with the TMA-AM algorithm enables real-time synchronization, monitoring, and unified data management, significantly enhancing the efficiency and accuracy of the FCST. Note to Practitioners—To address the inefficiencies and low monitoring quality associated with traditional manual testing methods in flight control system testing (FCST), we firstly introduce digital twin (DT) technology to this process. To support effective and accurate measurement during the FCST, we propose a vision-based method tailored to accurately measure deflection angles of flight control surfaces. This method replaces manual measurements with a non-contact approach, significantly improving measurement accuracy and efficiency. We provide a detailed description of the construction process of the DT-FCST platform including requirement analysis, DT model construction, and on-site experiments. This DT-based approach achieves real-time synchronization between virtual and physical testing processes, enhancing monitoring quality and overall testing effectiveness. Specifically, it can achieve a 90% reduction in the number of operators and shorten the single testing time to 16.7% of the traditional testing method.

Health-related social control refers to intentional attempts to influence people's health behaviors, often seen in personal relationships. Social robots hold promise in influencing people's health by exerting health-related social control, but it is unclear which social control strategies used by robots are appropriate and potentially effective. This study investigates the effects of positive versus negative, and relationship-oriented versus target-oriented social control strategies from a social robot on people's psychological reactions. In an online video prototype study, participants viewed scenarios of a social robot attempting to change their sedentary behaviors by using different strategies. We found that positive (versus negative) strategies elicited stronger positive affect, enjoyment, and perceived social appropriateness, reduced perceived threats to freedom, and strengthened behavioral intention. Meanwhile, the relationship-oriented (versus target-oriented) strategies elevated people's negative affect, reduced enjoyment and perceived appropriateness, elevated perceived threats to freedom, and weakened behavioral intentions. Given these findings, we give recommendations for designing health influence strategies in social robots.

During the flight control system testing (FCST), it is crucial to accurately measure the deflection angles of flight control surfaces to determine whether they respond precisely to commands. However, the traditional measurement method, which relies on the manual use of angle measuring rulers for inspections, is inefficient, prone to wear, and lacks precision. To address this issue, we introduce a vision-based angle measurement method for FCST that replaces manual measurements, thereby significantly enhancing testing efficiency and accuracy. Our proposed triangular mesh alignment based angle measurement algorithm (TMA-AM) is a non-contact measurement method that involves two key procedures, capturing 3D coordinates from images and calculating deflection angles. The TMA-AM algorithm converts deflected angles into angular differences between two coordinate systems, while accounting for the curved characteristics of control surfaces. We evaluate TMA-AM algorithm on a 3D-printed wing test platform and an aircraft wing test platform. Experimental results demonstrate that our method achieves high accuracy, with an average angular measurement error below 0.05○.

Inverse molecular design with diffusion models holds great potential for advancements in material and drug discovery. Despite success in unconditional molecule generation, integrating multiple properties such as synthetic score and gas permeability as condition constraints into diffusion models remains unexplored. We present the Graph Diffusion Transformer (Graph DiT) for multi-conditional molecular generation. Graph DiT has a condition encoder to learn the representation of numerical and categorical properties and utilizes a Transformer-based graph denoiser to achieve molecular graph denoising under conditions. Unlike previous graph diffusion models that add noise separately on the atoms and bonds in the forward diffusion process, we propose a graph-dependent noise model for training Graph DiT, designed to accurately estimate graph-related noise in molecules. We extensively validate the Graph DiT for multi-conditional polymer and small molecule generation. Results demonstrate our superiority across metrics from distribution learning to condition control for molecular properties. A polymer inverse design task for gas separation with feedback from domain experts further demonstrates its practical utility. The code is available at https://github.com/liugangcode/Graph-DiT.

Graph property prediction tasks are important and numerous. While each task offers a small size of labeled examples, unlabeled graphs have been collected from various sources and at a large scale. A conventional approach is training a model with the unlabeled graphs on self-supervised tasks and then fine-tuning the model on the prediction tasks. However, the self-supervised task knowledge could not be aligned or sometimes conflicted with what the predictions needed. In this paper, we propose to extract the knowledge underlying the large set of unlabeled graphs as a specific set of useful data points to augment each property prediction model. We use a diffusion model to fully utilize the unlabeled graphs and design two new objectives to guide the model's denoising process with each task's labeled data to generate task-specific graph examples and their labels. Experiments demonstrate that our data-centric approach performs significantly better than fifteen existing various methods on fifteen tasks. The performance improvement brought by unlabeled data is visible as the generated labeled examples unlike the self-supervised learning.

Rationale is defined as a subset of input features that best explains or supports the prediction by machine learning models. Rationale identification has improved the generalizability and interpretability of neural networks on vision and language data. In graph applications such as molecule and polymer property prediction, identifying representative subgraph structures named as graph rationales plays an essential role in the performance of graph neural networks. Existing graph pooling and/or distribution intervention methods suffer from the lack of examples to learn to identify optimal graph rationales. In this work, we introduce a new augmentation operation called environment replacement that automatically creates virtual data examples to improve rationale identification. We propose an efficient framework that performs rationale-environment separation and representation learning on the real and augmented examples in latent spaces to avoid the high complexity of explicit graph decoding and encoding. Comparing against recent techniques, experiments on seven molecular and four polymer datasets demonstrate the effectiveness and efficiency of the proposed augmentation-based graph rationalization framework. Data and the implementation of the proposed framework are publicly available https://github.com/liugangcode/GREA.