Neurorehabilitation conventionally relies on the interaction between a patient and a physical therapist. Robotic systems can improve and enrich the physical feedback provided to patients after neurological injury, but they under-utilize the adaptability and clinical expertise of trained therapists. In this position paper, we advocate for a novel approach that integrates the therapist's clinical expertise and nuanced decision-making with the strength, accuracy, and repeatability of robotics: Robot-mediated physical Human-Human Interaction. This framework, which enables two individuals to physically interact through robotic devices, has been studied across diverse research groups and has recently emerged as a promising link between conventional manual therapy and rehabilitation robotics, harmonizing the strengths of both approaches. This paper presents the rationale of a multidisciplinary team-including engineers, doctors, and physical therapists-for conducting research that utilizes: a unified taxonomy to describe robot-mediated rehabilitation, a framework of interaction based on social psychology, and a technological approach that makes robotic systems seamless facilitators of natural human-human interaction.

People with paralysis can control robotic devices through thought alone. Researchers at UC San Francisco have achieved a remarkable breakthrough in brain-computer interface (BCI) technology, enabling individuals with paralysis to control robotic devices through thought alone. This innovation combines artificial intelligence (AI) with neuroscience, allowing a paralyzed man to manipulate a robotic arm by imagining movements, a feat that marks a significant milestone in restoring autonomy to people with severe motor impairments. The device, known as a brain-computer interface (BCI), represents a fusion of advanced AI and neural engineering. BCIs have previously struggled to maintain functionality over extended periods, often losing effectiveness after just one or two days.

The U.S. Border Patrol agent killed in a shootout with armed suspects Monday has been identified as 44-year-old David Maland, a Customs and Border Protection source told Fox News. The veteran agent died Monday after a traffic stop on Interstate 91 between Newport and Orleans, Vermont, around 3:15 p.m. Monday, about 20 miles south of the U.S.-Canada border, according to the Department of Homeland Security. "A Border Patrol agent assigned to the US Border Patrol's Swanton Sector was fatally shot in the line of duty," acting DHS Secretary Benjamine Huffman said in a statement. "Every single day, our Border Patrol agents put themselves in harm's way so that Americans and our homeland are safe and secure." Wide shot of the scene on southbound Route 91 near Newport, Vermont, where a U.S. Border Patrol Agent was shot dead, Monday, January 20, 2025.

Hand-wearable robots, specifically exoskeletons, are designed to aid hands in daily activities, playing a crucial role in post-stroke rehabilitation and assisting the elderly. Our contribution to this field is a textile robotic glove with integrated actuators. These actuators, powered by pneumatic pressure, guide the user's hand to a desired position. Crafted from textile materials, our soft robotic glove prioritizes safety, lightweight construction, and user comfort. Utilizing the ruffles technique, integrated actuators guarantee high performance in blocking force and bending effectiveness. Additionally, we present a participant study confirming the effectiveness of our robotic device.

Hand-wearable robots, specifically exoskeletons, are designed to aid hands in daily activities, playing a crucial role in post-stroke rehabilitation and assisting the elderly. Our contribution to this field is a textile robotic glove with integrated actuators. These actuators, powered by pneumatic pressure, guide the user's hand to a desired position. Crafted from textile materials, our soft robotic glove prioritizes safety, lightweight construction, and user comfort. Utilizing the ruffles technique, integrated actuators guarantee high performance in blocking force and bending effectiveness. Here, we present a participant study confirming the effectiveness of our robotic device on a healthy participant group, exploiting EMG sensing.

History is filled with examples of robotic devices designed to reduce, eliminate, or improve upon human labor. From washing machines to roaming vacuum cleaners, various machines have transformed the way we work and live. Today, far more sophisticated service robots are wheeling into the picture, aiming to take humans out of the labor loop and, in the process, improve the speed and efficiency of interactions. They can carry plates between a restaurant kitchen and diners' tables, deliver a toothbrush to someone on the 28th floor of a hotel, and ensure a hospital patient receives her medications on time. They also are adept at stocking shelves, taking orders at a fast food restaurant, and serving as emotional companions.

Robotic technology has the potential to revolutionize the field of neurology by providing new methods for diagnosis, treatment, and rehabilitation of neurological disorders. In recent years, there has been an increasing interest in the development of robotics applications for neurology, driven by advances in sensing, actuation, and control systems. This review paper provides a comprehensive overview of the recent advancements in robotics technology for neurology, with a focus on three main areas: diagnosis, treatment, and rehabilitation. In the area of diagnosis, robotics has been used for developing new imaging techniques and tools for more accurate and non-invasive mapping of brain structures and functions. For treatment, robotics has been used for developing minimally invasive surgical procedures, including stereotactic and endoscopic approaches, as well as for the delivery of therapeutic agents to specific targets in the brain. In rehabilitation, robotics has been used for developing assistive devices and platforms for motor and cognitive training of patients with neurological disorders. The paper also discusses the challenges and limitations of current robotics technology for neurology, including the need for more reliable and precise sensing and actuation systems, the development of better control algorithms, and the ethical implications of robotic interventions in the human brain. Finally, the paper outlines future directions and opportunities for robotics applications in neurology, including the integration of robotics with other emerging technologies, such as neuroprosthetics, artificial intelligence, and virtual reality. Overall, this review highlights the potential of robotics technology to transform the field of neurology and improve the lives of patients with neurological disorders.

Robotic sensorimotor extensions (supernumerary limbs, prosthesis, handheld tools) are worn devices used to interact with the nearby environment, whether to assist the capabilities of impaired users or to enhance the dexterity of industrial operators. Despite numerous mechanical achievements, embedding these robotics devices remains critical due to their weight and discomfort. To emancipate from these mechanical constraints, we propose a new hybrid system using a virtually worn robotic arm in augmented-reality, and a real robotic manipulator servoed on such virtual representation. We aim at bringing an illusion of wearing a robotic system while its weight is fully deported, thinking that this approach could open new horizons for the study of wearable robotics without any intrinsic impairment of the human movement abilities.

Associate Professor of the Department of Information Technologies and Computer Sciences at MISIS University, Ph.D., mathematician and doctor Alexandra Bernadotte has developed algorithms that significantly increase the accuracy of recognition of mental commands by robotic devices. The result is achieved by optimizing the selection of a dictionary. Algorithms implemented in robotic devices can be used to transmit information through noisy communication channels. The results have been published in the peer-reviewed international scientific journal Mathematics. The task of improving the object (audio, video or electromagnetic signals) classification accuracy, when compiling so-called "dictionaries" of devices is faced by developers of different systems aimed to improve the quality of human life.

Associate Professor of the Department of Information Technologies and Computer Sciences at MISIS University, Ph.D., mathematician and doctor Alexandra Bernadotte has developed algorithms that significantly increase the accuracy of recognition of mental commands by robotic devices. The result is achieved by optimizing the selection of a dictionary. Algorithms implemented in robotic devices can be used to transmit information through noisy communication channels. The results have been published in the peer-reviewed international scientific journal Mathematics. The task of improving the object (audio, video or electromagnetic signals) classification accuracy, when compiling so-called "dictionaries" of devices is faced by developers of different systems aimed to improve the quality of human life.