Personalization is critical for the advancement of service robots. Robots need to develop tailored understandings of the environments they are put in. Moreover, they need to be aware of changes in the environment to facilitate long-term deployment. Long-Term understanding as well as personalization is necessary to execute complex tasks like prepare dinner table or tidy my room. A precursor to such tasks is that of Object Search. Consequently, this paper focuses on locating and searching multiple objects in indoor environments. In this paper, we propose two crucial novelties. Firstly, we propose a novel framework that can enable robots to deduce Personalized Ontologies of indoor environments. Our framework consists of a personalization schema that enables the robot to tune its understanding of ontologies. Secondly, we propose an Adaptive Inferencing strategy. We integrate Dynamic Belief Updates into our approach which improves performance in multi-object search tasks. The cumulative effect of personalization and adaptive inferencing is an improved capability in long-term object search. This framework is implemented on top of a multi-layered semantic map. We conduct experiments in real environments and compare our results against various stateof-the-art (SOTA) methods to demonstrate the effectiveness of our approach. Additionally, we show that personalization can act as a catalyst to enhance the performance of SOTAs. Video Link: https://bit.ly/3WHk9i9

We present a novel framework for estimating accident-prone regions in everyday indoor scenes, aimed at improving real-time risk awareness in service robots operating in human-centric environments. As robots become integrated into daily life, particularly in homes, the ability to anticipate and respond to environmental hazards is crucial for ensuring user safety, trust, and effective human-robot interaction. Our approach models object-level risk and context through a semantic graph-based propagation algorithm. Each object is represented as a node with an associated risk score, and risk propagates asymmetrically from high-risk to low-risk objects based on spatial proximity and accident relationship. This enables the robot to infer potential hazards even when they are not explicitly visible or labeled. Designed for interpretability and lightweight onboard deployment, our method is validated on a dataset with human-annotated risk regions, achieving a binary risk detection accuracy of 75%. The system demonstrates strong alignment with human perception, particularly in scenes involving sharp or unstable objects. These results underline the potential of context-aware risk reasoning to enhance robotic scene understanding and proactive safety behaviors in shared human-robot spaces. This framework could serve as a foundation for future systems that make context-driven safety decisions, provide real-time alerts, or autonomously assist users in avoiding or mitigating hazards within home environments.

This paper presents a standing support mobility robot "Moby" developed to enhance independence and safety for elderly individuals during daily activities such as toilet transfers. Unlike conventional seated mobility aids, the robot maintains users in an upright posture, reducing physical strain, supporting natural social interaction at eye level, and fostering a greater sense of self-efficacy. Moby offers a novel alternative by functioning both passively and with mobility support, enabling users to perform daily tasks more independently. Its main advantages include ease of use, lightweight design, comfort, versatility, and effective sit-to-stand assistance. The robot leverages the Robot Operating System (ROS) for seamless control, featuring manual and autonomous operation modes. A custom control system enables safe and intuitive interaction, while the integration with NAV2 and LiDAR allows for robust navigation capabilities. This paper reviews existing mobility solutions and compares them to Moby, details the robot’s design, and presents objective and subjective experimental results using the NASA-TLX method and time comparisons to other methods to validate our design criteria and demonstrate the advantages of our contribution.Video: https://bit.ly/moby-robot

Recent advancements in medical science and technology have led to a remarkable increase in the lifespan of the global elderly population. However, this demographic often struggles with mobility issues, leading to a sedentary lifestyle fueled by concerns over their physical capabilities. Traditional treadmill gait training, although beneficial, often becomes monotonous and lacks the real-world feedback necessary for engaging and effective rehabilitation. Addressing this gap, research into virtual walking systems utilizing avatar robots has gained traction. Despite the progress, several challenges remain where the systems prioritize visual feedback without considering the crucial need for alerting users to potential dangers and obstacles. This lack of comprehensive environmental awareness and feedback undermines both user engagement and safety. To address this problem, this paper proposes an algorithm that employs B-splines for precise free space detection, integrated with a safety stop mechanism for an avatar robot. This novel approach enhances user awareness of their surroundings through a sophisticated Graphic User Interface (GUI) that leverages Augmented Reality (AR) technology. By superimposing free space boundaries and warning messages directly onto a real-time camera feed, the system provides an intuitive and immersive navigation aid. The efficacy of our proposed GUI was rigorously tested across a series of realistic scenarios, comparing teleoperation control performance with and without the augmented interface. Our findings reveal that our GUI markedly enhances user safety and navigational effectiveness, fostering a deeper understanding of and interaction with the surrounding environment, thereby redefining user experience in virtual mobility assistance.

The ongoing COVID-19 pandemic has enforced governments across the world to impose social restrictions on the movement of people and confined them to their homes to avoid the spread of the disease. This not only forbids them from leaving their homes but also greatly reduces their physical activities. This situation has brought attention to virtual technologies such as virtual tours or telepresence robots. While these technologies allow people to remotely participate in activities, it does not address the problem of reduction in physical activities due to the pandemic. In this paper, we propose a telepresence robotic system driven by the user's gait to provide an immersive virtual walking experience in remote locations. To this end, we developed a control interface consisting of an automated treadmill that adjusts its speed to the user's pace automatically. This interface is used to control an avatar robot that sends a 360-degree live image back to the user for visual feedback. We conducted an evaluation experiment to compare the experience using the proposed system in two different conditions to that of regular walking. The results indicated that the proposed system gives an immersive and realistic virtual walking experience while demanding physical effort from the user.

Certain wheeled mobile robots e.g., electric wheelchairs, can operate through indirect joystick controls from users. Correct steering angle becomes essential when the user should determine the vehicle direction and velocity, in particular for differential wheeled vehicles since the vehicle velocity and direction are controlled with only two actuating wheels. This problem gets more challenging when complex curves should be realized by the user. A novel assistive controller with safety constraints is needed to address these problems. Also, the classic control methods mostly require the desired states beforehand which completely contradicts human's spontaneous decisions on the desired location to go. In this work, we develop a novel assistive control strategy based on differential geometry relying on only joystick inputs and vehicle states where the controller does not require any desired states. We begin with explaining the vehicle kinematics and our designed Darboux frame kinematics on a contact point of a virtual wheel and plane. Next, the geometric controller using the Darboux frame kinematics is designed for having smooth trajectories under certain safety constraints. We experiment our approach with different participants and evaluate its performance in various routes.

Quantifying the safety of the human body ori-entation is an important issue in human-robot interaction. Knowing the changing physical constraints on human motion can improve inspection of safe human motions and bring essential information about stability and normality of human body orientations with real-time risk assessment. Also, this information can be used in cooperative robots and monitoring systems to evaluate and interact in the environment more freely. Furthermore, the workspace area can be more deterministic with the known physical characteristics of safety. Based on this motivation, we propose a novel predictive safety model (PSM) that relies on the information of an inertial measurement unit on the human chest. The PSM encompasses a 3-Dofs spring-damper pendulum model that predicts human motion based on a safe motion dataset. The estimated safe orientation of humans is obtained by integrating a safety dataset and an elastic spring-damper model in a way that the proposed approach can realize complex motions at different safety levels. We did experiments in a real-world scenario to verify our novel proposed model. This novel approach can be used in different guidance/assistive robots and health monitoring systems to support and evaluate the human condition, particularly elders.