Articles tagged with Human Robot Interaction
The robot can switch between physical space and cyberspace modes using bioelectrical signals and gaze information, managing arm hand systems and IoT devices with high success rate. This technology aims to reduce nursing care costs and medical expenses by enhancing independence.
Researchers developed a lighter, smarter magnetoreceptive e-skin that tracks signal paths for applications like virtual reality and robotic systems. The new technology emulates the functioning of real skin and saves energy by using a single global sensor surface and central processing unit.
A study from Reichman University's School of Communications found that a robotic dog can influence leadership dynamics between humans by displaying clear preferences for team members. This can lead to spontaneous emergence of leadership, with the favored participant taking charge without explicit guidance.
Researchers found that people demonstrate a significant level of obedience towards humanoid robots, but it is slightly lower than towards humans. The study also showed that work efficiency under robot supervision is lower, with participants completing tasks more slowly and less effectively.
A new framework allows users to correct a robot's actions in real-time using intuitive interactions, such as pointing or nudging the arm. The method achieves a success rate of 21% higher than an alternative approach, enabling more efficient and accurate task completion.
Johns Hopkins engineers developed a pioneering prosthetic hand that can grip and grasp everyday objects like a human, using a hybrid design that combines rigid and soft robotics. The system achieves 99.69% accuracy in handling objects of varying textures and materials.
Researchers at UC3M developed a new soft joint model that enables versatility of movement, adaptability, and safety in robots. The asymmetrical triangular structure allows for greater bending angles with less force, providing operational protection and increased safety in human-robot interactions.
Researchers are developing a software framework for crowd-sourced 3D map generation and visual localization from camera data to improve real-time updates and low-cost visual localization. This technology aims to advance self-driving vehicles and enable fully automated transportation
Researchers developed a robot that mimics the bunting motion of a cat rubbing its head against a person, which has been shown to have a healing effect on humans. The robot's variable stiffness mechanism was found to be most effective in reducing tension among participants.
The Cambridge Handbook of the Law, Policy, and Regulation for Human-Robot Interaction addresses emerging issues in AI and robots, including privacy, safety, and regulation. The book offers valuable insights into ethical dilemmas and proposes solutions to balance enforceability and flexibility.
A study published in Science Robotics found that diverse and inclusive teams in robotics research achieve higher motivation, commitment, and productivity. The team identified seven main benefits of workforce diversity and inclusive leadership, including increased innovation and reduced bias.
Researchers created a measurement scale to assess robot human likeness, revealing four key qualities: appearance, emotional capacity, social intelligence, and self-understanding. To seem lifelike, robots must exhibit these traits, with self-understanding being the most challenging aspect to simulate.
The NUS research team has developed flexible fibres with self-healing, light-emitting and magnetic properties. The Scalable Hydrogel-clad Ionotronic Nickel-core Electroluminescent (SHINE) fibre offers a more efficient, durable and versatile alternative to existing light-emitting fibres.
Researchers developed a platform to help AI learn complex tasks through nuance and real-time instruction, achieving up to a 30% increase in success rates. The GUIDE framework allows humans to provide ongoing, nuanced feedback, fostering incremental improvements and deeper understanding.
Researchers discovered that synchronized movement between humans and robots builds trust, which can improve the success of human-robot teams. The study found that users who reported lower trust in the robots mirrored their movements less, suggesting that co-movement could be used to detect problems with user trust.
A review paper explores how robots can aid in understanding the human sense of self, including simulating mind and brain processes and testing social capacities. Researchers aim to transfer some features of human self-awareness to robots, which could provide insights into its development and potential applications.
Researchers developed a multimodal dataset, TimelyTale, to gather passenger-specific sensor data for context-relevant explanations. The approach effectively identified the timing and frequency of passenger demands for explanations, enabling the creation of a machine-learning model to predict the best time for providing an explanation.
Researchers used a virtual cow game to study human movement and navigation, developing a model that can simulate human behavior and predict choices. The study found that humans make decisions based on angular distance and previous choices, and the developed model could accurately mimic these patterns.
A new system dubbed SonicSense allows robots to interpret the world through acoustic vibrations, giving them a richer ability to 'feel' and understand objects. The system, featuring a robotic hand with contact microphones, can identify materials, shapes, and recognize objects in complex environments.
A new knee exoskeleton has been developed to support the quadriceps muscles during lifting tasks, helping workers maintain better posture even when fatigued. The device, which uses a complex algorithm to predict assistance needs, enabled participants to lift faster and with improved posture.
A study of 498 participants found that humans disapprove of hidden state deceptions but approve of external state deceptions that protect someone's feelings. The researchers suggest regulation to protect users from harmful deceptions and recommend further experimentation with real-life reactions.
The Human AugmentatioN via Dexterity (HAND) center aims to develop robots capable of enhancing human labor through engineered systems of dexterous robotic hands, AI-powered fine motor skills, and human interface. The center's goal is to make robotic assistance accessible and applicable to a wide range of physical actions.
Researchers will create versatile and easy-to-integrate robots capable of intelligent grasping, fine motor skills, and hand-eye coordination. The goal is to empower diverse workforces with robotic solutions, improving worker productivity and job opportunities.
A new algorithm developed at Washington State University improves safety and efficiency in robots working with humans by accounting for human carelessness. The tool has shown a maximum improvement of 80% in safety and 38% in efficiency compared to existing methods, and the researchers plan to test it in real-world settings.
Researchers have created a novel system called ConTac, which can estimate the shape and contact of a robotic arm with soft skin using a single sensing module. The system consists of a backbone, soft skin with markers, a camera to observe skin deformation, and models for shape and contact sensing.
Researchers found that socially assistive robots improved stroke rehabilitation outcomes by encouraging people to continue their treatment in a non-judgmental setting. Participants who used the robots showed significant improvements in kinematic and clinical measures, including smoothness of movement and upper-extremity assessments.
A team of researchers is creating a socially assistive model for a robotic dog that adapts its behavior based on the owner's physiological and emotional characteristics. The project aims to improve interactions between humans and robots in home and healthcare settings.
A humanoid robot has been trained to learn and perform various expressive movements, including simple dance routines and gestures. The enhanced expressiveness and agility of the robot pave the way for improving human-robot interactions in settings such as factory assembly lines, hospitals, and homes.
The ZEN-MRI project, funded by the German Ministry of Education and Research, aims to achieve a congenial co-existence between human beings and robots in public areas. Researchers will investigate how to design robots that are perceived as unthreatening and sympathetic.
The University of Maryland team created a camera mechanism that mimics the involuntary movements of the human eye, resulting in sharper and more accurate images. The Artificial Microsaccade-Enhanced Event Camera (AMI-EV) has implications for robotics, national defense, and industries relying on accurate image capture.
Researchers found that humans feel jointly a sense of agency with humanoid robots when they perceive them as intentional and social agents. This teamwork is more likely to occur when the robot displays human-like behavior and emotions.
Scientists at UVA and Toyota Research Institute create language representations of driving behavior to enable robots to associate words with environmental interactions. This allows cars to provide guidance and adjust speed in challenging situations, improving safety and usability.
A study found that responses with both visual and tactile feedback were most effective in conveying emotions on social media. Participants experienced greater feelings of support and approval when receiving tactile emoticons compared to visual-only feedback alone.
Researchers developed a novel methodology utilizing digital twins to establish the usefulness of built environment design guidelines for robots. Digital twins allow for real-time monitoring, hazard identification, and training a robot's algorithm before deployment.
Researchers at Uppsala University have developed an artificial tactile system that can detect pressure by touch in a similar way to the human nervous system. The technology has the potential to restore lost functionality to patients after a stroke, as well as enhance interactions between humans and robots.
Research at Tampere University aims to develop adaptable safety systems for autonomous off-road mobile machinery. The framework focuses on ensuring public safety without compromising technological advancements, incorporating a human-in-the-loop safety option.
A new online curriculum and simulation training program, developed by Penn State researchers, significantly reduced complications in central line placement. The program, launched in 2022, has been shown to decrease error rates for mechanical issues, infections, and blood clots.
Researchers at the University of Sydney and Queensland University of Technology have developed a new approach to designing cameras that process and scramble visual information. The approach, known as 'sighted systems,' creates distorted images that can still be used by robots to complete tasks but do not compromise privacy.
A team of Columbia engineers created Emo, a robotic face that makes eye contact and uses AI to anticipate and replicate human smiles. The robot can predict facial expressions and execute them simultaneously with humans, reducing disingenuous interactions.
Researchers at University of Missouri are developing software that allows drones to fly independently, perceiving and interacting with their environment while achieving specific goals. This technology has the potential to assist in mapping and monitoring applications, such as 3D or 4D advanced imagery for disaster response.
Researchers at Georgia Tech have developed a universal approach to controlling robotic exoskeletons that requires no training, calibration, or adjustments. The system uses deep learning to autonomously adjust assistance levels for walking, standing, and climbing stairs, reducing user effort and metabolic expenditure.
A portable robotic device has been developed to improve walking function in stroke survivors by altering gait asymmetry. The study, published in IEEE Transactions on Neural Systems and Rehabilitation Engineering, reveals that the exoskeleton can effectively train individuals to modify their walking asymmetry.
The study investigates relationships between customer equity drivers and trust in social robots, finding that effective customer service creates value equity. Businesses can benefit from social robots by enhancing relationship equity and brand equity, leading to greater trust and positive customer experiences. However, the negative asp...
The USC team created a low-cost, customizable learning kit for students to build their own 'robot friend' using the Blossom robot. The three-part module provides hands-on experience and instruction on various AI aspects, including robotics, machine learning, and software engineering.
A temperature-sensitive prosthetic limb has been developed to improve amputee interactions and feelings of human connection. Researchers have created a device called MiniTouch that provides realistic and real-time thermal feedback, enabling amputees to discriminate between objects of different temperatures and materials.
A study found that people prefer social robots speaking the same dialect as them, and those who are comfortable with a local accent trust robots that speak it more. The researchers suggest that this is because familiarity and similarity contribute to trustworthiness and competence perceptions.
A study redevelops a framework for teaching artificial intelligence and robotics to preschool children, providing five big ideas that can be grasped by young kids. The framework aims to prepare children to understand and use AI tools effectively, promoting healthy and responsible learning in the rapidly developing digital society.
A soft, wearable robot was used to help a person living with Parkinson’s disease walk without freezing, eliminating the debilitating symptom and allowing them to regain their independence. The device provided instantaneous effects and consistently improved walking in a range of conditions.
Researchers found that gentle electric currents on the back of the head improved skills in virtual reality and real-world surgical settings. Participants with stimulation showed a notable boost in dexterity, transferring skills from simulation to actual robot use.
Researchers at EPFL report on controlling a third arm with diaphragm movement, enhancing human cognition and exploring nervous system limitations. The study demonstrates intuitive control of an extra limb and potential applications in rehabilitation protocols.
A new robot grasping algorithm based on deep reinforcement learning (RGRL) is proposed to safely grasp objects from users' hands. The algorithm incorporates domain randomization and a multi-objective reward function, eliminating the need for manual labeling of data and reducing computational costs.
A Washington State University study found that watching videos of a soft robot working with a person at picking and placing tasks lowered the viewers' safety concerns and feelings of job insecurity. Soft robots have a potential psychological advantage over rigid robots, as proximity does not increase negative reactions.
Researchers at Princeton University and Google have developed a new technique to teach robots to ask for help when they're unsure. The method uses large language models to gauge uncertainty in complex environments, allowing robots to reduce the amount of help required while maintaining high accuracy.
Researchers at ETH Zurich developed an autonomous excavator called HEAP to construct a 6-meter-high and 65-meter-long dry-stone wall. The excavator uses sensors, machine vision, and algorithms to place stones in the desired location, achieving a high level of precision and speed.
The team developed a set of 11 actions allowing the robotic arm to pick up a wide variety of foods, including soups and pre-cut pizza. The system uses user-input images to identify foods and applies unique trajectories for scooping motions or wiggling inside food items.
A novel robotic system developed by USC researchers can help clinicians accurately assess a patient's rehabilitation progress. The method generates an 'arm nonuse' metric using machine learning and a socially assistive robot to track how much a patient is using their weaker arm spontaneously.
A team of researchers at Penn State found that a moderate talking speed for digital assistants increases user likelihood to use them. Conversation-like interactions also mitigate negative effects and increase user trust.
Researchers at MIT found that similarity-focused generative AI models falter when tasked with designing new products, highlighting the need to prioritize innovation in engineering tasks. By adjusting training objectives and metrics, AI can be an effective 'co-pilot' for engineers, enabling faster creation of innovative products.
Researchers found that humans who worked with robots were more likely to catch fewer defects later in the task, indicating a 'looking but not seeing' effect. This could lead to safety implications and negative impacts on work outcomes if not addressed in real-world environments.
Researchers discovered that users' prior beliefs about an AI chatbot's motives significantly impact their interactions with the agent. Priming users to believe certain things about the AI's empathy, neutrality, or manipulation influences their perception of its trustworthiness and effectiveness.