1,417 research outputs found
Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary
The complex physical properties of highly deformable materials such as
clothes pose significant challenges fanipulation systems. We present a novel
visual feedback dictionary-based method for manipulating defoor autonomous
robotic mrmable objects towards a desired configuration. Our approach is based
on visual servoing and we use an efficient technique to extract key features
from the RGB sensor stream in the form of a histogram of deformable model
features. These histogram features serve as high-level representations of the
state of the deformable material. Next, we collect manipulation data and use a
visual feedback dictionary that maps the velocity in the high-dimensional
feature space to the velocity of the robotic end-effectors for manipulation. We
have evaluated our approach on a set of complex manipulation tasks and
human-robot manipulation tasks on different cloth pieces with varying material
characteristics.Comment: The video is available at goo.gl/mDSC4
Learning to Navigate Cloth using Haptics
We present a controller that allows an arm-like manipulator to navigate
deformable cloth garments in simulation through the use of haptic information.
The main challenge of such a controller is to avoid getting tangled in, tearing
or punching through the deforming cloth. Our controller aggregates force
information from a number of haptic-sensing spheres all along the manipulator
for guidance. Based on haptic forces, each individual sphere updates its target
location, and the conflicts that arise between this set of desired positions is
resolved by solving an inverse kinematic problem with constraints.
Reinforcement learning is used to train the controller for a single
haptic-sensing sphere, where a training run is terminated (and thus penalized)
when large forces are detected due to contact between the sphere and a
simplified model of the cloth. In simulation, we demonstrate successful
navigation of a robotic arm through a variety of garments, including an
isolated sleeve, a jacket, a shirt, and shorts. Our controller out-performs two
baseline controllers: one without haptics and another that was trained based on
large forces between the sphere and cloth, but without early termination.Comment: Supplementary video available at https://youtu.be/iHqwZPKVd4A.
Related publications http://www.cc.gatech.edu/~karenliu/Robotic_dressing.htm
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