Quantifying the Quality of Haptic Interfaces
Shape-Changing Haptic Interfaces
Generating Clear Vibrotactile Cues with Magnets Embedded in a Soft Finger Sheath
Salient Full-Fingertip Haptic Feedback Enabled by Wearable Electrohydraulic Actuation
Cutaneous Electrohydraulic (CUTE) Wearable Devices for Pleasant Broad-Bandwidth Haptic Cues
Modeling Finger-Touchscreen Contact during Electrovibration
Perception of Ultrasonic Friction Pulses
Vibrotactile Playback for Teaching Sensorimotor Skills in Medical Procedures
CAPT Motor: A Two-Phase Ironless Motor Structure
4D Intraoperative Surgical Perception: Anatomical Shape Reconstruction from Multiple Viewpoints
Visual-Inertial Force Estimation in Robotic Surgery
Enhancing Robotic Surgical Training
AiroTouch: Naturalistic Vibrotactile Feedback for Large-Scale Telerobotic Assembly
Optimization-Based Whole-Arm Teleoperation for Natural Human-Robot Interaction
Finger-Surface Contact Mechanics in Diverse Moisture Conditions
Computational Modeling of Finger-Surface Contact
Perceptual Integration of Contact Force Components During Tactile Stimulation
Dynamic Models and Wearable Tactile Devices for the Fingertips
Novel Designs and Rendering Algorithms for Fingertip Haptic Devices
Dimensional Reduction from 3D to 1D for Realistic Vibration Rendering
Prendo: Analyzing Human Grasping Strategies for Visually Occluded Objects
Learning Upper-Limb Exercises from Demonstrations
Minimally Invasive Surgical Training with Multimodal Feedback and Automatic Skill Evaluation
Efficient Large-Area Tactile Sensing for Robot Skin
Haptic Feedback and Autonomous Reflexes for Upper-limb Prostheses
Gait Retraining
Modeling Hand Deformations During Contact
Intraoperative AR Assistance for Robot-Assisted Minimally Invasive Surgery
Immersive VR for Phantom Limb Pain
Visual and Haptic Perception of Real Surfaces
Haptipedia
Gait Propulsion Trainer
TouchTable: A Musical Interface with Haptic Feedback for DJs
Exercise Games with Baxter
Intuitive Social-Physical Robots for Exercise
How Should Robots Hug?
Hierarchical Structure for Learning from Demonstration
Fabrication of HuggieBot 2.0: A More Huggable Robot
Learning Haptic Adjectives from Tactile Data
Feeling With Your Eyes: Visual-Haptic Surface Interaction
S-BAN
General Tactile Sensor Model
Insight: a Haptic Sensor Powered by Vision and Machine Learning
How does HuggieBot compare to a human hugging partner?
]), or a passive robot, or receiving no hug.Social touch is fundamental to human connection, providing emotional support and reducing stress. When we receive a comforting hug from a trusted person, our bodies respond with physiological changes that promote relaxation, including releasing oxytocin counterbalancing cortisol. However, in moments of acute stress, not everyone has access to a supportive human hug. This lack of social support touch inspired our work to design and iteratively improve HuggieBot [], the first interactive hugging robot with visual and haptic perception to answer the question: could a robot provide similar comfort to a human hugging partner?
In our latest study [], we investigated how different forms of social touch impact stress recovery by analyzing physiological, neurohormonal, and behavioral responses to various post-stress interactions. Participants underwent the Trier Social Stress Test (TSST), a well-established protocol for inducing acute psychosocial stress. Then, they experienced ten minutes with one of five post-stress conditions: no hug, a passive robot hug, an active robot hug (HuggieBot), a robotic woman hug, or a natural woman hug. After a rest period, all participants then experienced ten minutes with the active robot hug.
To assess the effects of these interactions, we collected salivary samples at seven different time points, which we later analyzed for oxytocin and cortisol. Participants completed surveys at eight different time points. We continuously measured their heart rate throughout the experiment. Additionally, we analyzed six hugging behavioral responses during the hug sessions to quantify how participants interacted with the hugging agent during each condition. Behavioral measurements we coded included the number of hugs participants chose to exchange with the hugging agent, the number of times participants rested their head on the hugging agent, the percent time (of the ten minutes) they spent hugging the agent, and the minimum, average, and maximum time spent hugging the agent. By combining physiological, hormonal, and behavioral data, we aim to uncover the extent to which robotic hugs can provide meaningful emotional support post-acute stress.
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