1,783 research outputs found
Recognising the Clothing Categories from Free-Configuration Using Gaussian-Process-Based Interactive Perception
In this paper, we propose a Gaussian Process- based interactive perception approach for recognising highly- wrinkled clothes. We have integrated this recognition method within a clothes sorting pipeline for the pre-washing stage of an autonomous laundering process. Our approach differs from reported clothing manipulation approaches by allowing the robot to update its perception confidence via numerous interactions with the garments. The classifiers predominantly reported in clothing perception (e.g. SVM, Random Forest) studies do not provide true classification probabilities, due to their inherent structure. In contrast, probabilistic classifiers (of which the Gaussian Process is a popular example) are able to provide predictive probabilities. In our approach, we employ a multi-class Gaussian Process classification using the Laplace approximation for posterior inference and optimising hyper-parameters via marginal likelihood maximisation. Our experimental results show that our approach is able to recognise unknown garments from highly-occluded and wrinkled con- figurations and demonstrates a substantial improvement over non-interactive perception approaches
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
Model-Free 3D Shape Control of Deformable Objects Using Novel Features Based on Modal Analysis
Shape control of deformable objects is a challenging and important robotic
problem. This paper proposes a model-free controller using novel 3D global
deformation features based on modal analysis. Unlike most existing controllers
using geometric features, our controller employs a physically-based deformation
feature by decoupling 3D global deformation into low-frequency mode shapes.
Although modal analysis is widely adopted in computer vision and simulation, it
has not been used in robotic deformation control. We develop a new model-free
framework for modal-based deformation control under robot manipulation.
Physical interpretation of mode shapes enables us to formulate an analytical
deformation Jacobian matrix mapping the robot manipulation onto changes of the
modal features. In the Jacobian matrix, unknown geometry and physical
properties of the object are treated as low-dimensional modal parameters which
can be used to linearly parameterize the closed-loop system. Thus, an adaptive
controller with proven stability can be designed to deform the object while
online estimating the modal parameters. Simulations and experiments are
conducted using linear, planar, and solid objects under different settings. The
results not only confirm the superior performance of our controller but also
demonstrate its advantages over the baseline method.Comment: Accepted by the IEEE Transactions on Robotics. The paper will appear
in the IEEE Transactions on Robotics. IEEE copyrigh
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