28,473 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
Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting
This paper proposes a single-shot approach for recognising clothing
categories from 2.5D features. We propose two visual features, BSP (B-Spline
Patch) and TSD (Topology Spatial Distances) for this task. The local BSP
features are encoded by LLC (Locality-constrained Linear Coding) and fused with
three different global features. Our visual feature is robust to deformable
shapes and our approach is able to recognise the category of unknown clothing
in unconstrained and random configurations. We integrated the category
recognition pipeline with a stereo vision system, clothing instance detection,
and dual-arm manipulators to achieve an autonomous sorting system. To verify
the performance of our proposed method, we build a high-resolution RGBD
clothing dataset of 50 clothing items of 5 categories sampled in random
configurations (a total of 2,100 clothing samples). Experimental results show
that our approach is able to reach 83.2\% accuracy while classifying clothing
items which were previously unseen during training. This advances beyond the
previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach
in an autonomous robot sorting system, in which the robot recognises a clothing
item from an unconstrained pile, grasps it, and sorts it into a box according
to its category. Our proposed sorting system achieves reasonable sorting
success rates with single-shot perception.Comment: 9 pages, accepted by IROS201
Autonomous clothes manipulation using a hierarchical vision architecture
This paper presents a novel robot vision architecture for perceiving generic 3-D clothes configurations. Our architecture is hierarchically structured, starting from low-level curvature features to mid-level geometric shapes and topology descriptions, and finally, high-level semantic surface descriptions. We demonstrate our robot vision architecture in a customized dual-arm industrial robot with our inhouse developed stereo vision system, carrying out autonomous grasping and dual-arm flattening. The experimental results show the effectiveness of the proposed dual-arm flattening using the stereo vision system compared with the single-arm flattening using the widely cited Kinect-like sensor as the baseline. In addition, the proposed grasping approach achieves satisfactory performance when grasping various kind of garments, verifying the capability of the proposed visual perception architecture to be adapted to more than one clothing manipulation tasks
Private military services in the UK and Germany: Between partnership and regulation1
Controversial cases such as the aborted coup in Equatorial Guinea and the employment of private contractors in the Abu Ghraib prison have brought the proliferation of private ‘mercenaries’ to worldwide attention. However, the privatization of military security is more diverse and complex than generally suggested. Specifically, one needs to distinguish between the use of private mercenaries in developing countries and the privatization of military services in Europe. Focussing on the latter, this article proposes that the privatization of military services in industrialized countries can be understood in terms of a shift from ‘government’ to ‘governance’. As a consequence, the emergence of a private military industry in Europe is not only characterized by distinct forms of governance failure; European governments have also developed new modes of governance to control the industry. Using the United Kingdom and Germany as examples, this article examines two modes in particular: public private partnerships and governmental regulation
Harvesting Multiple Views for Marker-less 3D Human Pose Annotations
Recent advances with Convolutional Networks (ConvNets) have shifted the
bottleneck for many computer vision tasks to annotated data collection. In this
paper, we present a geometry-driven approach to automatically collect
annotations for human pose prediction tasks. Starting from a generic ConvNet
for 2D human pose, and assuming a multi-view setup, we describe an automatic
way to collect accurate 3D human pose annotations. We capitalize on constraints
offered by the 3D geometry of the camera setup and the 3D structure of the
human body to probabilistically combine per view 2D ConvNet predictions into a
globally optimal 3D pose. This 3D pose is used as the basis for harvesting
annotations. The benefit of the annotations produced automatically with our
approach is demonstrated in two challenging settings: (i) fine-tuning a generic
ConvNet-based 2D pose predictor to capture the discriminative aspects of a
subject's appearance (i.e.,"personalization"), and (ii) training a ConvNet from
scratch for single view 3D human pose prediction without leveraging 3D pose
groundtruth. The proposed multi-view pose estimator achieves state-of-the-art
results on standard benchmarks, demonstrating the effectiveness of our method
in exploiting the available multi-view information.Comment: CVPR 2017 Camera Read
Motion Planning of Uncertain Ordinary Differential Equation Systems
This work presents a novel motion planning framework, rooted in nonlinear programming theory, that treats uncertain fully and under-actuated dynamical systems described by ordinary differential equations. Uncertainty in multibody dynamical systems comes from various sources, such as: system parameters, initial conditions, sensor and actuator noise, and external forcing. Treatment of uncertainty in design is of paramount practical importance because all real-life systems are affected by it, and poor robustness and suboptimal performance result if it’s not accounted for in a given design. In this work uncertainties are modeled using Generalized Polynomial Chaos and are solved quantitatively using a least-square collocation method. The computational efficiency of this approach enables the inclusion of uncertainty statistics in the nonlinear programming optimization process. As such, the proposed framework allows the user to pose, and answer, new design questions related to uncertain dynamical systems.
Specifically, the new framework is explained in the context of forward, inverse, and hybrid dynamics formulations. The forward dynamics formulation, applicable to both fully and under-actuated systems, prescribes deterministic actuator inputs which yield uncertain state trajectories. The inverse dynamics formulation is the dual to the forward dynamic, and is only applicable to fully-actuated systems; deterministic state trajectories are prescribed and yield uncertain actuator inputs. The inverse dynamics formulation is more computationally efficient as it requires only algebraic evaluations and completely avoids numerical integration. Finally, the hybrid dynamics formulation is applicable to under-actuated systems where it leverages the benefits of inverse dynamics for actuated joints and forward dynamics for unactuated joints; it prescribes actuated state and unactuated input trajectories which yield uncertain unactuated states and actuated inputs.
The benefits of the ability to quantify uncertainty when planning the motion of multibody dynamic systems are illustrated through several case-studies. The resulting designs determine optimal motion plans—subject to deterministic and statistical constraints—for all possible systems within the probability space
Neutral buoyancy testing of a shuttle orbiter crew compartment
For abstract, se
LiveCap: Real-time Human Performance Capture from Monocular Video
We present the first real-time human performance capture approach that
reconstructs dense, space-time coherent deforming geometry of entire humans in
general everyday clothing from just a single RGB video. We propose a novel
two-stage analysis-by-synthesis optimization whose formulation and
implementation are designed for high performance. In the first stage, a skinned
template model is jointly fitted to background subtracted input video, 2D and
3D skeleton joint positions found using a deep neural network, and a set of
sparse facial landmark detections. In the second stage, dense non-rigid 3D
deformations of skin and even loose apparel are captured based on a novel
real-time capable algorithm for non-rigid tracking using dense photometric and
silhouette constraints. Our novel energy formulation leverages automatically
identified material regions on the template to model the differing non-rigid
deformation behavior of skin and apparel. The two resulting non-linear
optimization problems per-frame are solved with specially-tailored
data-parallel Gauss-Newton solvers. In order to achieve real-time performance
of over 25Hz, we design a pipelined parallel architecture using the CPU and two
commodity GPUs. Our method is the first real-time monocular approach for
full-body performance capture. Our method yields comparable accuracy with
off-line performance capture techniques, while being orders of magnitude
faster
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