15,241 research outputs found
Mid-air haptic rendering of 2D geometric shapes with a dynamic tactile pointer
An important challenge that affects ultrasonic midair haptics, in contrast to physical touch, is that we lose certain exploratory procedures such as contour following. This makes the task of perceiving geometric properties and shape identification more difficult. Meanwhile, the growing interest in mid-air haptics and their application to various new areas requires an improved understanding of how we perceive specific haptic stimuli, such as icons and control dials in mid-air. We address this challenge
by investigating static and dynamic methods of displaying 2D geometric shapes in mid-air. We display a circle, a square, and a triangle, in either a static or dynamic condition, using ultrasonic mid-air haptics. In the static condition, the shapes are presented as a full outline in mid-air, while in the dynamic condition, a tactile pointer is moved around the perimeter of the shapes. We measure participants’ accuracy and confidence of identifying
shapes in two controlled experiments (n1 = 34, n2 = 25). Results reveal that in the dynamic condition people recognise shapes significantly more accurately, and with higher confidence. We also find that representing polygons as a set of individually drawn haptic strokes, with a short pause at the corners, drastically enhances shape recognition accuracy. Our research supports the design of mid-air haptic user interfaces in application scenarios
such as in-car interactions or assistive technology in education
Tactile Mapping and Localization from High-Resolution Tactile Imprints
This work studies the problem of shape reconstruction and object localization
using a vision-based tactile sensor, GelSlim. The main contributions are the
recovery of local shapes from contact, an approach to reconstruct the tactile
shape of objects from tactile imprints, and an accurate method for object
localization of previously reconstructed objects. The algorithms can be applied
to a large variety of 3D objects and provide accurate tactile feedback for
in-hand manipulation. Results show that by exploiting the dense tactile
information we can reconstruct the shape of objects with high accuracy and do
on-line object identification and localization, opening the door to reactive
manipulation guided by tactile sensing. We provide videos and supplemental
information in the project's website
http://web.mit.edu/mcube/research/tactile_localization.html.Comment: ICRA 2019, 7 pages, 7 figures. Website:
http://web.mit.edu/mcube/research/tactile_localization.html Video:
https://youtu.be/uMkspjmDbq
Prediction of Human Trajectory Following a Haptic Robotic Guide Using Recurrent Neural Networks
Social intelligence is an important requirement for enabling robots to
collaborate with people. In particular, human path prediction is an essential
capability for robots in that it prevents potential collision with a human and
allows the robot to safely make larger movements. In this paper, we present a
method for predicting the trajectory of a human who follows a haptic robotic
guide without using sight, which is valuable for assistive robots that aid the
visually impaired. We apply a deep learning method based on recurrent neural
networks using multimodal data: (1) human trajectory, (2) movement of the
robotic guide, (3) haptic input data measured from the physical interaction
between the human and the robot, (4) human depth data. We collected actual
human trajectory and multimodal response data through indoor experiments. Our
model outperformed the baseline result while using only the robot data with the
observed human trajectory, and it shows even better results when using
additional haptic and depth data.Comment: 6 pages, Submitted to IEEE World Haptics Conference 201
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