16,055 research outputs found
Logical Learning Through a Hybrid Neural Network with Auxiliary Inputs
The human reasoning process is seldom a one-way process from an input leading
to an output. Instead, it often involves a systematic deduction by ruling out
other possible outcomes as a self-checking mechanism. In this paper, we
describe the design of a hybrid neural network for logical learning that is
similar to the human reasoning through the introduction of an auxiliary input,
namely the indicators, that act as the hints to suggest logical outcomes. We
generate these indicators by digging into the hidden information buried
underneath the original training data for direct or indirect suggestions. We
used the MNIST data to demonstrate the design and use of these indicators in a
convolutional neural network. We trained a series of such hybrid neural
networks with variations of the indicators. Our results show that these hybrid
neural networks are very robust in generating logical outcomes with inherently
higher prediction accuracy than the direct use of the original input and output
in apparent models. Such improved predictability with reassured logical
confidence is obtained through the exhaustion of all possible indicators to
rule out all illogical outcomes, which is not available in the apparent models.
Our logical learning process can effectively cope with the unknown unknowns
using a full exploitation of all existing knowledge available for learning. The
design and implementation of the hints, namely the indicators, become an
essential part of artificial intelligence for logical learning. We also
introduce an ongoing application setup for this hybrid neural network in an
autonomous grasping robot, namely as_DeepClaw, aiming at learning an optimized
grasping pose through logical learning.Comment: 11 pages, 9 figures, 4 table
Conserved Quantities and the Algebra of Braid Excitations in Quantum Gravity
We derive conservation laws from interactions of braid-like excitations of
embedded framed spin networks in Quantum Gravity. We also demonstrate that the
set of stable braid-like excitations form a noncommutative algebra under braid
interaction, in which the set of actively-interacting braids is a subalgebra.Comment: 26 pages, discussion expanded, accepted by Nucl. Phys.
DeepICP: An End-to-End Deep Neural Network for 3D Point Cloud Registration
We present DeepICP - a novel end-to-end learning-based 3D point cloud
registration framework that achieves comparable registration accuracy to prior
state-of-the-art geometric methods. Different from other keypoint based methods
where a RANSAC procedure is usually needed, we implement the use of various
deep neural network structures to establish an end-to-end trainable network.
Our keypoint detector is trained through this end-to-end structure and enables
the system to avoid the inference of dynamic objects, leverages the help of
sufficiently salient features on stationary objects, and as a result, achieves
high robustness. Rather than searching the corresponding points among existing
points, the key contribution is that we innovatively generate them based on
learned matching probabilities among a group of candidates, which can boost the
registration accuracy. Our loss function incorporates both the local similarity
and the global geometric constraints to ensure all above network designs can
converge towards the right direction. We comprehensively validate the
effectiveness of our approach using both the KITTI dataset and the
Apollo-SouthBay dataset. Results demonstrate that our method achieves
comparable or better performance than the state-of-the-art geometry-based
methods. Detailed ablation and visualization analysis are included to further
illustrate the behavior and insights of our network. The low registration error
and high robustness of our method makes it attractive for substantial
applications relying on the point cloud registration task.Comment: 10 pages, 6 figures, 3 tables, typos corrected, experimental results
updated, accepted by ICCV 201
SeeThruFinger: See and Grasp Anything with a Soft Touch
We present SeeThruFinger, a soft robotic finger with an in-finger vision for
multi-modal perception, including visual perception and tactile sensing, for
geometrically adaptive and real-time reactive grasping. Multi-modal perception
of intrinsic and extrinsic interactions is critical in building intelligent
robots that learn. Instead of adding various sensors for different modalities,
a preferred solution is to integrate them into one elegant and coherent design,
which is a challenging task. This study leverages the Soft Polyhedral Network
design as a robotic finger, capable of omni-directional adaptation with an
unobstructed view of the finger's spatial deformation from the inside. By
embedding a miniature camera underneath, we achieve the visual perception of
the external environment by inpainting the finger mask using E2FGV, which can
be used for object detection in the downstream tasks for grasping. After
contacting the objects, we use real-time object segmentation algorithms, such
as XMem, to track the soft finger's spatial deformations. We also learned a
Supervised Variational Autoencoder to enable tactile sensing of 6D forces and
torques for reactive grasp. As a result, we achieved multi-modal perception,
including visual perception and tactile sensing, and soft, adaptive object
grasping within a single vision-based soft finger design compatible with
multi-fingered robotic grippers.Comment: 10 pages, 5 figures, 1 table, submitted to Soft Robotics under revie
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