1,513 research outputs found
Data-Driven Grasp Synthesis - A Survey
We review the work on data-driven grasp synthesis and the methodologies for
sampling and ranking candidate grasps. We divide the approaches into three
groups based on whether they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
On Recognizing Transparent Objects in Domestic Environments Using Fusion of Multiple Sensor Modalities
Current object recognition methods fail on object sets that include both
diffuse, reflective and transparent materials, although they are very common in
domestic scenarios. We show that a combination of cues from multiple sensor
modalities, including specular reflectance and unavailable depth information,
allows us to capture a larger subset of household objects by extending a state
of the art object recognition method. This leads to a significant increase in
robustness of recognition over a larger set of commonly used objects.Comment: 12 page
Safe Robotic Grasping: Minimum Impact-Force Grasp Selection
This paper addresses the problem of selecting from a choice of possible
grasps, so that impact forces will be minimised if a collision occurs while the
robot is moving the grasped object along a post-grasp trajectory. Such
considerations are important for safety in human-robot interaction, where even
a certified "human-safe" (e.g. compliant) arm may become hazardous once it
grasps and begins moving an object, which may have significant mass, sharp
edges or other dangers. Additionally, minimising collision forces is critical
to preserving the longevity of robots which operate in uncertain and hazardous
environments, e.g. robots deployed for nuclear decommissioning, where removing
a damaged robot from a contaminated zone for repairs may be extremely difficult
and costly. Also, unwanted collisions between a robot and critical
infrastructure (e.g. pipework) in such high-consequence environments can be
disastrous. In this paper, we investigate how the safety of the post-grasp
motion can be considered during the pre-grasp approach phase, so that the
selected grasp is optimal in terms applying minimum impact forces if a
collision occurs during a desired post-grasp manipulation. We build on the
methods of augmented robot-object dynamics models and "effective mass" and
propose a method for combining these concepts with modern grasp and trajectory
planners, to enable the robot to achieve a grasp which maximises the safety of
the post-grasp trajectory, by minimising potential collision forces. We
demonstrate the effectiveness of our approach through several experiments with
both simulated and real robots.Comment: To be appeared in IEEE/RAS IROS 201
Visual Perception of Garments for their Robotic Manipulation
TĂ©matem pĹ™edloĹľenĂ© práce je strojovĂ© vnĂmánĂ textiliĂ zaloĹľenĂ© na obrazovĂ© informaci a vyuĹľitĂ© pro jejich robotickou manipulaci. Práce studuje nÄ›kolik reprezentativnĂch textiliĂ v běžnĂ˝ch kognitivnÄ›-manipulaÄŤnĂch Ăşlohách, jako je napĹ™Ăklad tĹ™ĂdÄ›nĂ neznámĂ˝ch odÄ›vĹŻ podle typu nebo jejich skládánĂ. NÄ›kterĂ© z tÄ›chto ÄŤinnostĂ by v budoucnu mohly bĂ˝t vykonávány domácĂmi robotickĂ˝mi pomocnĂky. Strojová manipulace s textiliemi je poptávaná takĂ© v prĹŻmyslu. HlavnĂ vĂ˝zvou Ĺ™ešenĂ©ho problĂ©mu je mÄ›kkost a s tĂm souvisejĂcĂ vysoká deformovatelnost textiliĂ, kterĂ© se tak mohou nacházet v bezpoÄŤtu vizuálnÄ› velmi odlišnĂ˝ch stavĹŻ.The presented work addresses the visual perception of garments applied for their robotic manipulation. Various types of garments are considered in the typical perception and manipulation tasks, including their classification, folding or unfolding. Our work is motivated by the possibility of having humanoid household robots performing these tasks for us in the future, as well as by the industrial applications. The main challenge is the high deformability of garments, which can be posed in infinitely many configurations with a significantly varying appearance
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
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