This study introduces a new force-position impedance control strategy tailored for robotic systems engaged in the manipulation of elastic garments. Our approach is innovative for incorporating external force and impedance control within an incremental trajectory generation framework. This method allows for dynamic adjustment to prevent over-deformation and potential damage during garment handling, enabling the fabric to deform to desired states and follow intended trajectory trends without compromising textile integrity. By utilizing external forces in an adaptive, incremental manner, we offer a nuanced solution for expected garment manipulation. Our strategy demonstrates the feasibility of detailed manipulation tasks in industrial settings, bridging the gap between simulated environments and real-world operations. This effort underscores the importance of automation in enhancing textile handling processes, focusing on practical applications rather than theoretical advancements.

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

This research unveils a novel Multi-Critic Reinforcement Learning framework designed to navigate the multifaceted challenges associated with multi-phased garment handling tasks, notably marked by persistent contact and erratic deformations between textiles and solid bodies. These tasks, ubiquitous in domestic and industrial environments, encompass activities such as dressing, fabric printing, and pressing, and are complicated by the unpredictability of textile states and the intricacy of devising control strategies. Our reinforcement learning model combines multiple time-sequenced Critic networks with traditional Deep Deterministic Policy Gradient (DDPG) techniques, thereby equipping the system to adapt to the diverse effects of fabric distortions throughout various stages. The effectiveness of this approach is demonstrated through a multi-phase pre-printing operation and further validated by real-world implementations, showing significant improvements in coverage and a substantial reduction in wrinkle formation, with its versatility further confirmed by a complex vertical dressing task. We anticipate future applications of this framework in a range of complex problems, not just garment handling. The model used in this paper can be found at https://github.com/jkk5454/multiddpg.git.

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.

Robotic automation is steadily growing in different industries around the world. However, in some industries, such as garment manufacturing, most tasks are still predominantly manual, due to the flexible nature of clothes. Garment and clothes are easily deformed when some force is applied, so it is difficult for robots to handle them while predicting their deformation. Our general research goal is to realize flexible cloth handling using robots to automate different tasks in the garment manufacturing industry. We draw inspiration from the actions that humans perform when manipulating clothes and emulate them using a robotic system. In this paper, we developed a robot hand with a camera at the finger, to obtain local information of the contact between the garment and the robot hand, in order to achieve garment handling tasks. Specifically, we focus on the pinch and slide motion that humans perform when straightening a piece of cloth. We selected a specific task to be automated and proposed three manipulation strategies to approach the garment using visual information from the finger camera that enabled the system to perform the task consistently. We carried out two validation experiments to demonstrate the effectiveness of the proposed methods, and an application experiment where we evaluate their applicability to a specific task.

For the visually impaired people, some outdoor activities like running or soccer are difficult, due to not being able to clearly see the environment. Recently, multiple researchers have contributed to help the visually impaired people run outdoors using robotic systems with different types of feedback, such as auditory feedback and haptic feedback. They discovered that using robotic systems can be an effective way to guide visually impaired people while exercising outdoors. In this paper, we propose a method to guide the visually impaired people to do sports outside using a robotic system with haptic feedback, and we evaluate the feasibility of the proposed system through experiments with blindfolded users running outdoors. In the running guidance task, the position of the runner is determined from the visual feed of a drone, and haptic feedback produced on the users’ left lower leg is used to convey to the runner the directions in which to move to remain on a specific path. Additionally, we compared the performance of users under different haptic feedback modalities in the running task. The three compared modalities are: producing vibration only during the swing phase, only during stance phase or producing vibration continuously. The experimental results for a running task showed that our system enabled users to remain inside a specified track 93% of the total running time, while the ratio decreased to 79%, 77%, and 61% when receiving vibrations during only swing phase, during only stance phase, and without using any feedback respectively. We also observed users felt safer while running blindfolded by using the proposed method.

In this paper we present a wearable Haptic Feedback Device to convey intuitive motion direction to the user through haptic feedback based on vibrotactile illusions. Vibrotactile illusions occur on the skin when two or more vibrotactile actuators in proximity are actuated in coordinated sequence, causing the user to feel combined sensations, instead of separate ones. By combining these illusions we can produce various sensation patterns that are discernible by the user, thus allowing to convey different information with each pattern. A method to provide information about direction through vibrotactile illusions is introduced on this paper. This method uses a grid of vibrotactile actuators around the arm actuated in coordination. The sensation felt on the skin is consistent with the desired direction of motion, so the desired motion can be intuitively understood. We show that the users can recognize the conveyed direction, and implemented a proof of concept of the proposed method to guide users' elbow flexion/extension motion.