Articles tagged with Human Robot Interaction
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.
Researchers at Queen Mary University of London and their colleagues have found that supernumerary robotic arms can be used as effectively as a partner, enabling tasks like opening doors while carrying packages. The study suggests these arms could aid people with various tasks, such as surgery or industrial work.
Researchers discovered that humans experience empathic embarrassment when witnessing robots in embarrassing situations. The study also found cognitive empathy was stronger for human actors compared to robot avatars.
Researchers created a robot-assisted dressing system using artificial intelligence to accommodate various human body shapes, arm poses, and clothing selections. The system was evaluated in a human study with 510 trials across 17 participants, achieving an average success rate of 86% in pulling the sleeve of each garment onto their arm.
A new sensor developed by Queen Mary University of London enhances robots' sense of touch, allowing them to accurately measure interaction forces and geometry. This breakthrough could pave the way for more advanced and reliable robotics in the future, enabling better handling and manipulation of objects.
A team of researchers at Harvard University has developed a compact, soft pump that can power soft robots in various applications. The pump uses dielectric elastomer actuators and can control pressure, flow rate, and flow direction, making it suitable for biomedical settings.
Researchers examine whether robots and AI can replicate the ethical concepts attributed to human nurses, including advocacy, accountability, cooperation, and caring. While AI can inform patients about medical errors and present treatment options, it questions its ability to truly understand and empathize with patients' values.
Researchers at Carnegie Mellon University have created a new model, Vision-Robotics Bridge, which enables robots to learn from observing humans complete tasks in any environment. The robots successfully learned 12 tasks, including opening drawers and picking up objects, with minimal practice time of just 25 minutes.
A communication robot is being developed to prevent elderly isolation by facilitating self-disclosure through different communication options. The study found that the most appropriate option depends on the recipient, message content, and sender's gender and personality traits, reducing participants' anxiety in self-disclosure.
Researchers from Carnegie Mellon University have created a fabric and sensing system, RobotSweater, that allows machines to better interact with humans. The knitted textile 'skin' can sense contact and pressure, enabling robots to move in response to human gestures.
Researchers identified three main topics: environmental perception, risk assessment and mitigation, and human factors and ergonomics in automated agricultural machines. The study highlights the need for improved sensors and safety standards to address the challenges posed by dynamic environments.
A Cornell University-led study using the Tetris game found that players who receive fewer turns perceive their partner as less likable, regardless of whether a human or AI allocates the turns. The researchers also discovered that machine allocation behavior can lead to worse performance and gameplay when fairness is not guaranteed.
Researchers at the University of Waterloo have developed a robot system that uses artificial intelligence to track objects, including medication and glasses. This technology has shown high accuracy in locating everyday items, potentially improving the quality of life for individuals with dementia.
Researchers at the University of Gothenburg found that advanced AI systems can lead to excessive suspicion and damage relationships due to uncertainty about conversational partner's intentions. The study highlights the need for transparent AI design, with well-functioning voices that clearly indicate they are synthetic.
Researchers developed ProTac, a soft robotic link with multimodal perception to improve human-robot interactions. The device incorporates tactile and proximity sensing capabilities, enabling robots to react safely and predictably to physical contact.
Four to eleven-year-olds deemed Alexa more human-like, but neither robot deserved to be harmed or yelled at. As kids advanced towards adolescence, the feeling dwindled, and older children reported it was slightly more acceptable to attack technology.
Researchers found that players' brains respond differently to human and machine opponents, with desynchronization indicating active calculations. The study suggests that training with machines may not replicate the experience of playing against humans.
A study on robot deception found that participants were overly trusting of AI and required explicit admission of lying in apologies to repair trust. Apologies that did not admit to deception were more effective at repairing trust, suggesting a need for designers to understand the ramifications of their design choices.
The CONVINCE project, funded by €4 million, focuses on developing cognitive deliberation capabilities in autonomous robots to enhance safe and robust operation in unstructured environments. The goal is to enable robots to adapt to their surroundings and tasks autonomously without human intervention.
The TOAST project, led by Aarhus University, aims to develop the Tactile Internet technology and upscale the talent pool for this emerging field. Researchers will focus on edge intelligence, haptic communication, and machine learning to create immersive user experiences.
A study by researchers from the University of Cambridge found that robots can be a useful tool to promote mental wellbeing in the workplace. The experiment used two different robot wellbeing coaches, with participants showing a better connection and positive perception with a toy-like robot compared to a humanoid-like robot.
The iCPH platform combines physical and cyber elements to capture human motions, using musculoskeletal analysis and machine learning. It generates contact motion networks for humanoid robots and simulates human behaviors, enabling smooth interactions with humans.