7 research outputs found
Towards Assistive Feeding with a General-Purpose Mobile Manipulator
General-purpose mobile manipulators have the potential to serve as a
versatile form of assistive technology. However, their complexity creates
challenges, including the risk of being too difficult to use. We present a
proof-of-concept robotic system for assistive feeding that consists of a Willow
Garage PR2, a high-level web-based interface, and specialized autonomous
behaviors for scooping and feeding yogurt. As a step towards use by people with
disabilities, we evaluated our system with 5 able-bodied participants. All 5
successfully ate yogurt using the system and reported high rates of success for
the system's autonomous behaviors. Also, Henry Evans, a person with severe
quadriplegia, operated the system remotely to feed an able-bodied person. In
general, people who operated the system reported that it was easy to use,
including Henry. The feeding system also incorporates corrective actions
designed to be triggered either autonomously or by the user. In an offline
evaluation using data collected with the feeding system, a new version of our
multimodal anomaly detection system outperformed prior versions.Comment: This short 4-page paper was accepted and presented as a poster on
May. 16, 2016 in ICRA 2016 workshop on 'Human-Robot Interfaces for Enhanced
Physical Interactions' organized by Arash Ajoudani, Barkan Ugurlu, Panagiotis
Artemiadis, Jun Morimoto. It was peer reviewed by one reviewe
Deep Haptic Model Predictive Control for Robot-Assisted Dressing
Robot-assisted dressing offers an opportunity to benefit the lives of many
people with disabilities, such as some older adults. However, robots currently
lack common sense about the physical implications of their actions on people.
The physical implications of dressing are complicated by non-rigid garments,
which can result in a robot indirectly applying high forces to a person's body.
We present a deep recurrent model that, when given a proposed action by the
robot, predicts the forces a garment will apply to a person's body. We also
show that a robot can provide better dressing assistance by using this model
with model predictive control. The predictions made by our model only use
haptic and kinematic observations from the robot's end effector, which are
readily attainable. Collecting training data from real world physical
human-robot interaction can be time consuming, costly, and put people at risk.
Instead, we train our predictive model using data collected in an entirely
self-supervised fashion from a physics-based simulation. We evaluated our
approach with a PR2 robot that attempted to pull a hospital gown onto the arms
of 10 human participants. With a 0.2s prediction horizon, our controller
succeeded at high rates and lowered applied force while navigating the garment
around a persons fist and elbow without getting caught. Shorter prediction
horizons resulted in significantly reduced performance with the sleeve catching
on the participants' fists and elbows, demonstrating the value of our model's
predictions. These behaviors of mitigating catches emerged from our deep
predictive model and the controller objective function, which primarily
penalizes high forces.Comment: 8 pages, 12 figures, 1 table, 2018 IEEE International Conference on
Robotics and Automation (ICRA
Multidimensional Capacitive Sensing for Robot-Assisted Dressing and Bathing
Robotic assistance presents an opportunity to benefit the lives of many
people with physical disabilities, yet accurately sensing the human body and
tracking human motion remain difficult for robots. We present a
multidimensional capacitive sensing technique that estimates the local pose of
a human limb in real time. A key benefit of this sensing method is that it can
sense the limb through opaque materials, including fabrics and wet cloth. Our
method uses a multielectrode capacitive sensor mounted to a robot's end
effector. A neural network model estimates the position of the closest point on
a person's limb and the orientation of the limb's central axis relative to the
sensor's frame of reference. These pose estimates enable the robot to move its
end effector with respect to the limb using feedback control. We demonstrate
that a PR2 robot can use this approach with a custom six electrode capacitive
sensor to assist with two activities of daily living-dressing and bathing. The
robot pulled the sleeve of a hospital gown onto able-bodied participants' right
arms, while tracking human motion. When assisting with bathing, the robot moved
a soft wet washcloth to follow the contours of able-bodied participants' limbs,
cleaning their surfaces. Overall, we found that multidimensional capacitive
sensing presents a promising approach for robots to sense and track the human
body during assistive tasks that require physical human-robot interaction.Comment: 8 pages, 16 figures, International Conference on Rehabilitation
Robotics 201
3D Human Pose Estimation on a Configurable Bed from a Pressure Image
Robots have the potential to assist people in bed, such as in healthcare
settings, yet bedding materials like sheets and blankets can make observation
of the human body difficult for robots. A pressure-sensing mat on a bed can
provide pressure images that are relatively insensitive to bedding materials.
However, prior work on estimating human pose from pressure images has been
restricted to 2D pose estimates and flat beds. In this work, we present two
convolutional neural networks to estimate the 3D joint positions of a person in
a configurable bed from a single pressure image. The first network directly
outputs 3D joint positions, while the second outputs a kinematic model that
includes estimated joint angles and limb lengths. We evaluated our networks on
data from 17 human participants with two bed configurations: supine and seated.
Our networks achieved a mean joint position error of 77 mm when tested with
data from people outside the training set, outperforming several baselines. We
also present a simple mechanical model that provides insight into ambiguity
associated with limbs raised off of the pressure mat, and demonstrate that
Monte Carlo dropout can be used to estimate pose confidence in these
situations. Finally, we provide a demonstration in which a mobile manipulator
uses our network's estimated kinematic model to reach a location on a person's
body in spite of the person being seated in a bed and covered by a blanket.Comment: 8 pages, 10 figure
3D Human Pose Estimation on a Configurable Bed from a Pressure Image
Robots have the potential to assist people in bed,
such as in healthcare settings, yet bedding materials like sheets
and blankets can make observation of the human body difficult
for robots. A pressure-sensing mat on a bed can provide pressure
images that are relatively insensitive to bedding materials.
However, prior work on estimating human pose from pressure
images has been restricted to 2D pose estimates and flat beds.
In this work, we present two convolutional neural networks to
estimate the 3D joint positions of a person in a configurable
bed from a single pressure image. The first network directly
outputs 3D joint positions, while the second outputs a kinematic
model that includes estimated joint angles and limb lengths. We
evaluated our networks on data from 17 human participants
with two bed configurations: supine and seated. Our networks
achieved a mean joint position error of 77 mm when tested
with data from people outside the training set, outperforming
several baselines. We also present a simple mechanical model
that provides insight into ambiguity associated with limbs raised
off of the pressure mat, and demonstrate that Monte Carlo
dropout can be used to estimate pose confidence in these
situations. Finally, we provide a demonstration in which a
mobile manipulator uses our networkās estimated kinematic
model to reach a location on a personās body in spite of the
person being seated in a bed and covered by a blanket
Multidimensional Capacitive Sensing for Robot-Assisted Dressing and Bathing
Robotic assistance presents an opportunity to beneļ¬t the lives of many people with physical disabilities, yet accurately sensing the human body and tracking human motion remain difļ¬cult for robots. We present a multidimensional capacitive sensing technique that estimates the local pose of a human limb in real time. A key beneļ¬t of this sensing method is that it can sense the limb through opaque materials, including fabrics and wet cloth. Our method uses a multielectrode capacitive sensor mounted to a robotās end effector. A neural network model estimates the position of the closest point on a personās limb and the orientation of the limbās central axis relative to the sensorās frame of reference. These pose estimates enable the robot to move its end effector with respect to the limb using feedback control. We demonstrate that a PR2 robot can use this approach with a custom six electrode capacitive sensor to assist with two activities of daily livingā dressing and bathing. The robot pulled the sleeve of a hospital gown onto able-bodied participantsā right arms, while tracking human motion. When assisting with bathing, the robot moved a soft wet washcloth to follow the contours of able-bodied participantsā limbs, cleaning their surfaces. Overall, we found that multidimensional capacitive sensing presents a promising approach for robots to sense and track the human body during assistive tasks that require physical human-robot interaction