5,456 research outputs found
Improved GelSight Tactile Sensor for Measuring Geometry and Slip
A GelSight sensor uses an elastomeric slab covered with a reflective membrane
to measure tactile signals. It measures the 3D geometry and contact force
information with high spacial resolution, and successfully helped many
challenging robot tasks. A previous sensor, based on a semi-specular membrane,
produces high resolution but with limited geometry accuracy. In this paper, we
describe a new design of GelSight for robot gripper, using a Lambertian
membrane and new illumination system, which gives greatly improved geometric
accuracy while retaining the compact size. We demonstrate its use in measuring
surface normals and reconstructing height maps using photometric stereo. We
also use it for the task of slip detection, using a combination of information
about relative motions on the membrane surface and the shear distortions. Using
a robotic arm and a set of 37 everyday objects with varied properties, we find
that the sensor can detect translational and rotational slip in general cases,
and can be used to improve the stability of the grasp.Comment: IEEE/RSJ International Conference on Intelligent Robots and System
Active Clothing Material Perception using Tactile Sensing and Deep Learning
Humans represent and discriminate the objects in the same category using
their properties, and an intelligent robot should be able to do the same. In
this paper, we build a robot system that can autonomously perceive the object
properties through touch. We work on the common object category of clothing.
The robot moves under the guidance of an external Kinect sensor, and squeezes
the clothes with a GelSight tactile sensor, then it recognizes the 11
properties of the clothing according to the tactile data. Those properties
include the physical properties, like thickness, fuzziness, softness and
durability, and semantic properties, like wearing season and preferred washing
methods. We collect a dataset of 153 varied pieces of clothes, and conduct 6616
robot exploring iterations on them. To extract the useful information from the
high-dimensional sensory output, we applied Convolutional Neural Networks (CNN)
on the tactile data for recognizing the clothing properties, and on the Kinect
depth images for selecting exploration locations. Experiments show that using
the trained neural networks, the robot can autonomously explore the unknown
clothes and learn their properties. This work proposes a new framework for
active tactile perception system with vision-touch system, and has potential to
enable robots to help humans with varied clothing related housework.Comment: ICRA 2018 accepte
Connecting Look and Feel: Associating the visual and tactile properties of physical materials
For machines to interact with the physical world, they must understand the
physical properties of objects and materials they encounter. We use fabrics as
an example of a deformable material with a rich set of mechanical properties. A
thin flexible fabric, when draped, tends to look different from a heavy stiff
fabric. It also feels different when touched. Using a collection of 118 fabric
sample, we captured color and depth images of draped fabrics along with tactile
data from a high resolution touch sensor. We then sought to associate the
information from vision and touch by jointly training CNNs across the three
modalities. Through the CNN, each input, regardless of the modality, generates
an embedding vector that records the fabric's physical property. By comparing
the embeddings, our system is able to look at a fabric image and predict how it
will feel, and vice versa. We also show that a system jointly trained on vision
and touch data can outperform a similar system trained only on visual data when
tested purely with visual inputs
Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision
In order to estimate the motion of an object, the visual system needs to combine multiple local measurements, each of which carries some degree of ambiguity. We present a model of motion perception whereby measurements from different image regions are combined according to a Bayesian estimator --- the estimated motion maximizes the posterior probability assuming a prior favoring slow and smooth velocities. In reviewing a large number of previously published phenomena we find that the Bayesian estimator predicts a wide range of psychophysical results. This suggests that the seemingly complex set of illusions arise from a single computational strategy that is optimal under reasonable assumptions
Reading between the (Ledger) Lines: Performing Mozart\u27s Music for the Basset Clarinet
RILM abstract: The basset horn came into being around 1788. Mozart used it for his concerto K.622 and quintet K.581, and for certain arias in Cosi fan tutte and La clemenza di Tito. Other works of Mozart may have been realized with the basset horn, particularly in performances by Anton Stadler
DeepKey: Towards End-to-End Physical Key Replication From a Single Photograph
This paper describes DeepKey, an end-to-end deep neural architecture capable
of taking a digital RGB image of an 'everyday' scene containing a pin tumbler
key (e.g. lying on a table or carpet) and fully automatically inferring a
printable 3D key model. We report on the key detection performance and describe
how candidates can be transformed into physical prints. We show an example
opening a real-world lock. Our system is described in detail, providing a
breakdown of all components including key detection, pose normalisation,
bitting segmentation and 3D model inference. We provide an in-depth evaluation
and conclude by reflecting on limitations, applications, potential security
risks and societal impact. We contribute the DeepKey Datasets of 5, 300+ images
covering a few test keys with bounding boxes, pose and unaligned mask data.Comment: 14 pages, 12 figure
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