35,747 research outputs found
The implications of embodiment for behavior and cognition: animal and robotic case studies
In this paper, we will argue that if we want to understand the function of
the brain (or the control in the case of robots), we must understand how the
brain is embedded into the physical system, and how the organism interacts with
the real world. While embodiment has often been used in its trivial meaning,
i.e. 'intelligence requires a body', the concept has deeper and more important
implications, concerned with the relation between physical and information
(neural, control) processes. A number of case studies are presented to
illustrate the concept. These involve animals and robots and are concentrated
around locomotion, grasping, and visual perception. A theoretical scheme that
can be used to embed the diverse case studies will be presented. Finally, we
will establish a link between the low-level sensory-motor processes and
cognition. We will present an embodied view on categorization, and propose the
concepts of 'body schema' and 'forward models' as a natural extension of the
embodied approach toward first representations.Comment: Book chapter in W. Tschacher & C. Bergomi, ed., 'The Implications of
Embodiment: Cognition and Communication', Exeter: Imprint Academic, pp. 31-5
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
Optical coherence tomography-based consensus definition for lamellar macular hole.
BackgroundA consensus on an optical coherence tomography definition of lamellar macular hole (LMH) and similar conditions is needed.MethodsThe panel reviewed relevant peer-reviewed literature to reach an accord on LMH definition and to differentiate LMH from other similar conditions.ResultsThe panel reached a consensus on the definition of three clinical entities: LMH, epiretinal membrane (ERM) foveoschisis and macular pseudohole (MPH). LMH definition is based on three mandatory criteria and three optional anatomical features. The three mandatory criteria are the presence of irregular foveal contour, the presence of a foveal cavity with undermined edges and the apparent loss of foveal tissue. Optional anatomical features include the presence of epiretinal proliferation, the presence of a central foveal bump and the disruption of the ellipsoid zone. ERM foveoschisis definition is based on two mandatory criteria: the presence of ERM and the presence of schisis at the level of Henle's fibre layer. Three optional anatomical features can also be present: the presence of microcystoid spaces in the inner nuclear layer (INL), an increase of retinal thickness and the presence of retinal wrinkling. MPH definition is based on three mandatory criteria and two optional anatomical features. Mandatory criteria include the presence of a foveal sparing ERM, the presence of a steepened foveal profile and an increased central retinal thickness. Optional anatomical features are the presence of microcystoid spaces in the INL and a normal retinal thickness.ConclusionsThe use of the proposed definitions may provide uniform language for clinicians and future research
Robotic Ironing with 3D Perception and Force/Torque Feedback in Household Environments
As robotic systems become more popular in household environments, the
complexity of required tasks also increases. In this work we focus on a
domestic chore deemed dull by a majority of the population, the task of
ironing. The presented algorithm improves on the limited number of previous
works by joining 3D perception with force/torque sensing, with emphasis on
finding a practical solution with a feasible implementation in a domestic
setting. Our algorithm obtains a point cloud representation of the working
environment. From this point cloud, the garment is segmented and a custom
Wrinkleness Local Descriptor (WiLD) is computed to determine the location of
the present wrinkles. Using this descriptor, the most suitable ironing path is
computed and, based on it, the manipulation algorithm performs the
force-controlled ironing operation. Experiments have been performed with a
humanoid robot platform, proving that our algorithm is able to detect
successfully wrinkles present in garments and iteratively reduce the
wrinkleness using an unmodified iron.Comment: Accepted and to be published on the 2017 IEEE/RSJ International
Conference on Intelligent Robots and Systems (IROS 2017) that will be held in
Vancouver, Canada, September 24-28, 201
Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting
This paper proposes a single-shot approach for recognising clothing
categories from 2.5D features. We propose two visual features, BSP (B-Spline
Patch) and TSD (Topology Spatial Distances) for this task. The local BSP
features are encoded by LLC (Locality-constrained Linear Coding) and fused with
three different global features. Our visual feature is robust to deformable
shapes and our approach is able to recognise the category of unknown clothing
in unconstrained and random configurations. We integrated the category
recognition pipeline with a stereo vision system, clothing instance detection,
and dual-arm manipulators to achieve an autonomous sorting system. To verify
the performance of our proposed method, we build a high-resolution RGBD
clothing dataset of 50 clothing items of 5 categories sampled in random
configurations (a total of 2,100 clothing samples). Experimental results show
that our approach is able to reach 83.2\% accuracy while classifying clothing
items which were previously unseen during training. This advances beyond the
previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach
in an autonomous robot sorting system, in which the robot recognises a clothing
item from an unconstrained pile, grasps it, and sorts it into a box according
to its category. Our proposed sorting system achieves reasonable sorting
success rates with single-shot perception.Comment: 9 pages, accepted by IROS201
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