An ageing population and the need of providing adequate care have led to developing robots to relieve healthcare workers and to assist individuals in their own homes. However, the successful integration of robots in such settings relies on more than just ensuring physical safety associated with physical risks (e.g., collisions): it also requires the user’s perceived safety – the users perceiving the robot as not doing any harm. This paper explores the potential influence of a robot gripper’s visual and tactile properties, such as materials and texture, on the users’ perceived safety and comfort of human-robot interaction. An initial survey was distributed to 53 participants, exploring five (n=5) robot gripper designs focusing on the robots’ gripper shape. One design shape was thereafter selected to be constructed as a cover to be placed over the parallel grippers of the TIAGo robot, by using 1) wood filament and 2) plastic. The covers were then tested in an experimental setting with 11 participants. The covers were attached to the TIAGo mobile manipulator robot and participants interacted with both of the designed gripper covers within a controlled laboratory environment. A questionnaire was distributed to all 11 experiment participants, at different stages of the interactions. The findings indicate that the material of the gripper influenced participants’ sense of comfort, familiarity, and perceived capabilities of the robot. The study suggests that perceived safety in human-robot interaction (HRI) is shaped not only by physical factors but also by how materials are personally and contextually interpreted. To better support safe and comfortable interactions, further research is needed to understand how material choices shape users’ perceived safety.

Human Activity Recognition (HAR) models often rely on small, specialized datasets, limiting their generalizability. In addition, many systems rely on privacy-invasive RGB video as their primary sensing modality. This choice raises ethical concerns, especially in health- and home-care robotics, where patient privacy is paramount. In this study, we evaluate transfer learning as a method to improve HAR generalizability across RGB, IMU, and depth imaging modalities while assessing how privacy-preserving modalities can compensate for a lack of RGB video in multimodal learning contexts.We train a feature-fusion model on aggregated HAR datasets, leveraging pretrained backbones for each modality, and compare it to a general multimodal model pretrained on non-HAR datasets. Evaluating on the PriMA-Care privacy-focused dataset across combinations of modalities, we find that the general model outperforms the HAR model, with best accuracies of 98.29% for the general model with RGB + IMU and 94.97% for the HAR-specific model with RGB + depth. Analysis shows that the general model more readily identifies individuals from RGB input, while IMU and depth better preserve privacy with a small accuracy loss (5%).

Striking a balance between utility and privacy holds significant importance in systems that rely on sensor utilization, such as robots. This balance is even more vital in care robots, given the sensitivity of personal data and the necessity for privacy-preserving monitoring to ensure user comfort. This paper presents a comprehensive investigation into the utility-privacy trade-off concerning different vision-based sensors. Specifically, RGB cameras, color and mono-color thermal cameras, and depth sensors are compared, considering technical aspects and users’ perception of privacy. The technical analysis addresses human pose tracking, human presence detection, human vital sign monitoring, and human facial and emotion recognition. The quantitative examination of sensors in real-life scenarios highlights the mono-color thermal camera’s effectiveness for user monitoring. Particularly, this sensor excels in challenging human presence detection scenarios compared to RGB cameras. Furthermore, interview and survey studies, encompassing two different age groups were carried out to compare how sensors are perceived in terms of user privacy. The quantitative and qualitative assessments of users’ feedback in these studies reveal that apart from depth sensors, thermal mono-color, and thermal color sensors are perceived as better at preserving user privacy compared to RGB cameras. The analysis includes the influence of participant age on privacy perception, indicating non-significant effects. Considering both the technical assessment and user preferences, the mono-color thermal camera emerges as the optimal choice for human monitoring purposes.

Successfully implementing robots to support senior adults requires their acceptance. Leveraging nonverbal communication could enhance the ease and intuitiveness of accepting robot assistance. However, it is essential to see how different age groups understand nonverbal communication cues to understand the dynamics between different user groups and assistive robots. Our research specifically delves into the intuitive understanding of handshaking gestures across multiple interactions, focusing on seniors (between 70 and 97) and young (21 and 26) adults. Through a combination of observations and open-ended surveys, we conducted a video observation and thematic analysis. Interestingly, our findings indicate no significant differences between the two age groups, except for reactions and interaction time variables. Furthermore, we report on possible motivations behind the initial reactions in the two age groups, familiarity, and ways to improve the overall Human-Robot Interaction experience potentially.

Robots have the potential to provide everyday life care and support for senior adults, but acceptance is essential for successful implementation in the domestic environment. Nonverbal social behavior can enhance this acceptance, and behavioral cues should be easy and intuitive to understand. However, which factors contribute to senior adults’ intuitive understanding of social cues, such as handshakes? Our research aims to address this question using video observations and semi-structured interviews. Based on a thematic analysis and video observations, our findings indicate that some participants intuitively understood how to shake hands. Most did not shake hands due to not understanding the robot’s behavior or fear. Other identified themes included: contributing features for intuitive handshakes, design improvements, and experiences with the robot’s end effector. Lastly, we found no significant effect between the initial response of the participants to the handshake and either the reaction time or the handshake duration. By designing the gripper and the robot itself in a more familiar, less fear-eliciting way, senior adults might understand the gesture of shaking hands more intuitively.

This paper presents a Nonlinear Model Predictive Control-based Reinforcement Learning (NMPC-based RL) framework for robot manipulators. The controller is developed to address the motion planning problem for robot manipulators in the presence of obstacles. The proposed control scheme includes a parametrized NMPC structure used as an approximator for the RL framework’s value function and action-value function. In the NMPC structure, the cost function, system constraints, and the manipulator’s model are parameterized. The Q-Learning algorithm based on the Temporal Difference method adjusts the parameters of the NMPC to increase the closed-loop performance of the whole control scheme. The controller has been applied to a 6-degrees-of-freedom (DoF) model of a robot manipulator, aimed at moving its end-effector to reach the desired pose when static obstacles are in the robot’s workspace. Numerical simulations demonstrate that the proposed controller can effectively control the end-effector’s pose in such a way as to avoid any collisions between the manipulator and the obstacles. It is shown that the learning capability of the proposed NMPC-based RL framework can enhance the efficiency of the control loop up to 21%.