53,303 research outputs found
A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering
The growing demand of industrial, automotive and service robots presents a
challenge to the centralized Cloud Robotics model in terms of privacy,
security, latency, bandwidth, and reliability. In this paper, we present a `Fog
Robotics' approach to deep robot learning that distributes compute, storage and
networking resources between the Cloud and the Edge in a federated manner. Deep
models are trained on non-private (public) synthetic images in the Cloud; the
models are adapted to the private real images of the environment at the Edge
within a trusted network and subsequently, deployed as a service for
low-latency and secure inference/prediction for other robots in the network. We
apply this approach to surface decluttering, where a mobile robot picks and
sorts objects from a cluttered floor by learning a deep object recognition and
a grasp planning model. Experiments suggest that Fog Robotics can improve
performance by sim-to-real domain adaptation in comparison to exclusively using
Cloud or Edge resources, while reducing the inference cycle time by 4\times to
successfully declutter 86% of objects over 213 attempts.Comment: IEEE International Conference on Robotics and Automation, ICRA, 201
Learning Accurate Extended-Horizon Predictions of High Dimensional Trajectories
We present a novel predictive model architecture based on the principles of
predictive coding that enables open loop prediction of future observations over
extended horizons. There are two key innovations. First, whereas current
methods typically learn to make long-horizon open-loop predictions using a
multi-step cost function, we instead run the model open loop in the forward
pass during training. Second, current predictive coding models initialize the
representation layer's hidden state to a constant value at the start of an
episode, and consequently typically require multiple steps of interaction with
the environment before the model begins to produce accurate predictions.
Instead, we learn a mapping from the first observation in an episode to the
hidden state, allowing the trained model to immediately produce accurate
predictions. We compare the performance of our architecture to a standard
predictive coding model and demonstrate the ability of the model to make
accurate long horizon open-loop predictions of simulated Doppler radar
altimeter readings during a six degree of freedom Mars landing. Finally, we
demonstrate a 2X reduction in sample complexity by using the model to implement
a Dyna style algorithm to accelerate policy learning with proximal policy
optimization
A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening
Pan-sharpening is a fundamental and significant task in the field of remote
sensing imagery processing, in which high-resolution spatial details from
panchromatic images are employed to enhance the spatial resolution of
multi-spectral (MS) images. As the transformation from low spatial resolution
MS image to high-resolution MS image is complex and highly non-linear, inspired
by the powerful representation for non-linear relationships of deep neural
networks, we introduce multi-scale feature extraction and residual learning
into the basic convolutional neural network (CNN) architecture and propose the
multi-scale and multi-depth convolutional neural network (MSDCNN) for the
pan-sharpening of remote sensing imagery. Both the quantitative assessment
results and the visual assessment confirm that the proposed network yields
high-resolution MS images that are superior to the images produced by the
compared state-of-the-art methods
Jacquard: A Large Scale Dataset for Robotic Grasp Detection
Grasping skill is a major ability that a wide number of real-life
applications require for robotisation. State-of-the-art robotic grasping
methods perform prediction of object grasp locations based on deep neural
networks. However, such networks require huge amount of labeled data for
training making this approach often impracticable in robotics. In this paper,
we propose a method to generate a large scale synthetic dataset with ground
truth, which we refer to as the Jacquard grasping dataset. Jacquard is built on
a subset of ShapeNet, a large CAD models dataset, and contains both RGB-D
images and annotations of successful grasping positions based on grasp attempts
performed in a simulated environment. We carried out experiments using an
off-the-shelf CNN, with three different evaluation metrics, including real
grasping robot trials. The results show that Jacquard enables much better
generalization skills than a human labeled dataset thanks to its diversity of
objects and grasping positions. For the purpose of reproducible research in
robotics, we are releasing along with the Jacquard dataset a web interface for
researchers to evaluate the successfulness of their grasping position
detections using our dataset
A Zero-Shot Learning application in Deep Drawing process using Hyper-Process Model
One of the consequences of passing from mass production to mass customization
paradigm in the nowadays industrialized world is the need to increase
flexibility and responsiveness of manufacturing companies. The high-mix /
low-volume production forces constant accommodations of unknown product
variants, which ultimately leads to high periods of machine calibration. The
difficulty related with machine calibration is that experience is required
together with a set of experiments to meet the final product quality.
Unfortunately, all possible combinations of machine parameters is so high that
is difficult to build empirical knowledge. Due to this fact, normally trial and
error approaches are taken making one-of-a-kind products not viable. Therefore,
a Zero-Shot Learning (ZSL) based approach called hyper-process model (HPM) to
learn the relation among multiple tasks is used as a way to shorten the
calibration phase. Assuming each product variant is a task to solve, first, a
shape analysis on data to learn common modes of deformation between tasks is
made, and secondly, a mapping between these modes and task descriptions is
performed. Ultimately, the present work has two main contributions: 1)
Formulation of an industrial problem into a ZSL setting where new process
models can be generated for process optimization and 2) the definition of a
regression problem in the domain of ZSL. For that purpose, a 2-d deep drawing
simulated process was used based on data collected from the Abaqus simulator,
where a significant number of process models were collected to test the
effectiveness of the approach. The obtained results show that is possible to
learn new tasks without any available data (both labeled and unlabeled) by
leveraging information about already existing tasks, allowing to speed up the
calibration phase and make a quicker integration of new products into
manufacturing systems.Comment: 25 pages, 8 figures, 2 tables and submitted to ACM Transactions on
Intelligent Systems and Technology. arXiv admin note: text overlap with
arXiv:1810.1033
Learning Physics-Based Manipulation in Clutter: Combining Image-Based Generalization and Look-Ahead Planning
Physics-based manipulation in clutter involves complex interaction between
multiple objects. In this paper, we consider the problem of learning, from
interaction in a physics simulator, manipulation skills to solve this
multi-step sequential decision making problem in the real world. Our approach
has two key properties: (i) the ability to generalize and transfer manipulation
skills (over the type, shape, and number of objects in the scene) using an
abstract image-based representation that enables a neural network to learn
useful features; and (ii) the ability to perform look-ahead planning in the
image space using a physics simulator, which is essential for such multi-step
problems. We show, in sets of simulated and real-world experiments (video
available on https://youtu.be/EmkUQfyvwkY), that by learning to evaluate
actions in an abstract image-based representation of the real world, the robot
can generalize and adapt to the object shapes in challenging real-world
environments
Informed Machine Learning -- A Taxonomy and Survey of Integrating Knowledge into Learning Systems
Despite its great success, machine learning can have its limits when dealing
with insufficient training data. A potential solution is the additional
integration of prior knowledge into the training process which leads to the
notion of informed machine learning. In this paper, we present a structured
overview of various approaches in this field. We provide a definition and
propose a concept for informed machine learning which illustrates its building
blocks and distinguishes it from conventional machine learning. We introduce a
taxonomy that serves as a classification framework for informed machine
learning approaches. It considers the source of knowledge, its representation,
and its integration into the machine learning pipeline. Based on this taxonomy,
we survey related research and describe how different knowledge representations
such as algebraic equations, logic rules, or simulation results can be used in
learning systems. This evaluation of numerous papers on the basis of our
taxonomy uncovers key methods in the field of informed machine learning.Comment: Accepted at IEEE Transactions on Knowledge and Data Engineering:
https://ieeexplore.ieee.org/document/942998
Robust X-ray Sparse-view Phase Tomography via Hierarchical Synthesis Convolutional Neural Networks
Convolutional Neural Networks (CNN) based image reconstruction methods have
been intensely used for X-ray computed tomography (CT) reconstruction
applications. Despite great success, good performance of this data-based
approach critically relies on a representative big training data set and a
dense convoluted deep network. The indiscriminating convolution connections
over all dense layers could be prone to over-fitting, where sampling biases are
wrongly integrated as features for the reconstruction. In this paper, we report
a robust hierarchical synthesis reconstruction approach, where training data is
pre-processed to separate the information on the domains where sampling biases
are suspected. These split bands are then trained separately and combined
successively through a hierarchical synthesis network. We apply the
hierarchical synthesis reconstruction for two important and classical
tomography reconstruction scenarios: the spares-view reconstruction and the
phase reconstruction. Our simulated and experimental results show that
comparable or improved performances are achieved with a dramatic reduction of
network complexity and computational cost. This method can be generalized to a
wide range of applications including material characterization, in-vivo
monitoring and dynamic 4D imaging.Comment: 9 pages, 6 figures, 2 table
System-Level Predictive Maintenance: Review of Research Literature and Gap Analysis
This paper reviews current literature in the field of predictive maintenance
from the system point of view. We differentiate the existing capabilities of
condition estimation and failure risk forecasting as currently applied to
simple components, from the capabilities needed to solve the same tasks for
complex assets. System-level analysis faces more complex latent degradation
states, it has to comprehensively account for active maintenance programs at
each component level and consider coupling between different maintenance
actions, while reflecting increased monetary and safety costs for system
failures. As a result, methods that are effective for forecasting risk and
informing maintenance decisions regarding individual components do not readily
scale to provide reliable sub-system or system level insights. A novel holistic
modeling approach is needed to incorporate available structural and physical
knowledge and naturally handle the complexities of actively fielded and
maintained assets.Comment: 24 pages, 3 figure
Learning-based Feedback Controller for Deformable Object Manipulation
In this paper, we present a general learning-based framework to automatically
visual-servo control the position and shape of a deformable object with unknown
deformation parameters. The servo-control is accomplished by learning a
feedback controller that determines the robotic end-effector's movement
according to the deformable object's current status. This status encodes the
object's deformation behavior by using a set of observed visual features, which
are either manually designed or automatically extracted from the robot's sensor
stream. A feedback control policy is then optimized to push the object toward a
desired featured status efficiently. The feedback policy can be learned either
online or offline. Our online policy learning is based on the Gaussian Process
Regression (GPR), which can achieve fast and accurate manipulation and is
robust to small perturbations. An offline imitation learning framework is also
proposed to achieve a control policy that is robust to large perturbations in
the human-robot interaction. We validate the performance of our controller on a
set of deformable object manipulation tasks and demonstrate that our method can
achieve effective and accurate servo-control for general deformable objects
with a wide variety of goal settings.Comment: arXiv admin note: text overlap with arXiv:1709.07218,
arXiv:1710.06947, arXiv:1802.0966
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