681 research outputs found
In-home and remote use of robotic body surrogates by people with profound motor deficits
By controlling robots comparable to the human body, people with profound
motor deficits could potentially perform a variety of physical tasks for
themselves, improving their quality of life. The extent to which this is
achievable has been unclear due to the lack of suitable interfaces by which to
control robotic body surrogates and a dearth of studies involving substantial
numbers of people with profound motor deficits. We developed a novel, web-based
augmented reality interface that enables people with profound motor deficits to
remotely control a PR2 mobile manipulator from Willow Garage, which is a
human-scale, wheeled robot with two arms. We then conducted two studies to
investigate the use of robotic body surrogates. In the first study, 15 novice
users with profound motor deficits from across the United States controlled a
PR2 in Atlanta, GA to perform a modified Action Research Arm Test (ARAT) and a
simulated self-care task. Participants achieved clinically meaningful
improvements on the ARAT and 12 of 15 participants (80%) successfully completed
the simulated self-care task. Participants agreed that the robotic system was
easy to use, was useful, and would provide a meaningful improvement in their
lives. In the second study, one expert user with profound motor deficits had
free use of a PR2 in his home for seven days. He performed a variety of
self-care and household tasks, and also used the robot in novel ways. Taking
both studies together, our results suggest that people with profound motor
deficits can improve their quality of life using robotic body surrogates, and
that they can gain benefit with only low-level robot autonomy and without
invasive interfaces. However, methods to reduce the rate of errors and increase
operational speed merit further investigation.Comment: 43 Pages, 13 Figure
A List of Household Objects for Robotic Retrieval Prioritized by People with ALS (Version 092008)
This technical report is designed to serve as a citable reference for the
original prioritized object list that the Healthcare Robotics Lab at Georgia
Tech released on its website in September of 2008. It is also expected to serve
as the primary citable reference for the research associated with this list
until the publication of a detailed, peer-reviewed paper.
The original prioritized list of object classes resulted from a needs
assessment involving 8 motor-impaired patients with amyotrophic lateral
sclerosis (ALS) and targeted, in-person interviews of 15 motor-impaired ALS
patients. All of these participants were drawn from the Emory ALS Center.
The prioritized object list consists of 43 object classes ranked by how
important the participants considered each class to be for retrieval by an
assistive robot. We intend for this list to be used by researchers to inform
the design and benchmarking of robotic systems, especially research related to
autonomous mobile manipulation
A Model that Predicts the Material Recognition Performance of Thermal Tactile Sensing
Tactile sensing can enable a robot to infer properties of its surroundings,
such as the material of an object. Heat transfer based sensing can be used for
material recognition due to differences in the thermal properties of materials.
While data-driven methods have shown promise for this recognition problem, many
factors can influence performance, including sensor noise, the initial
temperatures of the sensor and the object, the thermal effusivities of the
materials, and the duration of contact. We present a physics-based mathematical
model that predicts material recognition performance given these factors. Our
model uses semi-infinite solids and a statistical method to calculate an F1
score for the binary material recognition. We evaluated our method using
simulated contact with 69 materials and data collected by a real robot with 12
materials. Our model predicted the material recognition performance of support
vector machine (SVM) with 96% accuracy for the simulated data, with 92%
accuracy for real-world data with constant initial sensor temperatures, and
with 91% accuracy for real-world data with varied initial sensor temperatures.
Using our model, we also provide insight into the roles of various factors on
recognition performance, such as the temperature difference between the sensor
and the object. Overall, our results suggest that our model could be used to
help design better thermal sensors for robots and enable robots to use them
more effectively.Comment: This article is currently under review for possible publicatio
The Whole World in Your Hand: Active and Interactive Segmentation
Object segmentation is a fundamental problem
in computer vision and a powerful resource for
development. This paper presents three embodied approaches to the visual segmentation of objects. Each approach to segmentation is aided
by the presence of a hand or arm in the proximity of the object to be segmented. The first
approach is suitable for a robotic system, where
the robot can use its arm to evoke object motion. The second method operates on a wearable system, viewing the world from a human's
perspective, with instrumentation to help detect
and segment objects that are held in the wearer's
hand. The third method operates when observing
a human teacher, locating periodic motion (finger/arm/object waving or tapping) and using it
as a seed for segmentation. We show that object segmentation can serve as a key resource for
development by demonstrating methods that exploit high-quality object segmentations to develop
both low-level vision capabilities (specialized feature detectors) and high-level vision capabilities
(object recognition and localization)
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