38,674 research outputs found
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
The implications of noninertial motion on covariant quantum spin
It is shown that the Pauli-Lubanski spin vector defined in terms of
curvilinear co-ordinates does not satisfy Lorentz invariance for spin-1/2
particles in noninertial motion along a curved trajectory. The possibility of
detecting this violation in muon decay experiments is explored, where the
noninertial contribution to the decay rate becomes large for muon beams with
large momenta and trajectories with radius of curvature approaching the muon's
Compton wavelength scale. A new spacelike spin vector is derived from the
Pauli-Lubanski vector that satisfies Lorentz invariance for both inertial and
noninertial motion. In addition, this spin vector suggests a generalization for
the classification of spin-1/2 particles, and has interesting properties that
are applicable for both massive and massless particles.Comment: REVTeX file; 7 pages; 2 figures; slightly revised with new abstract;
accepted for publication in Classical and Quantum Gravit
Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks
It is common to implicitly assume access to intelligently captured inputs
(e.g., photos from a human photographer), yet autonomously capturing good
observations is itself a major challenge. We address the problem of learning to
look around: if a visual agent has the ability to voluntarily acquire new views
to observe its environment, how can it learn efficient exploratory behaviors to
acquire informative observations? We propose a reinforcement learning solution,
where the agent is rewarded for actions that reduce its uncertainty about the
unobserved portions of its environment. Based on this principle, we develop a
recurrent neural network-based approach to perform active completion of
panoramic natural scenes and 3D object shapes. Crucially, the learned policies
are not tied to any recognition task nor to the particular semantic content
seen during training. As a result, 1) the learned "look around" behavior is
relevant even for new tasks in unseen environments, and 2) training data
acquisition involves no manual labeling. Through tests in diverse settings, we
demonstrate that our approach learns useful generic policies that transfer to
new unseen tasks and environments. Completion episodes are shown at
https://goo.gl/BgWX3W
Perception-aware Path Planning
In this paper, we give a double twist to the problem of planning under
uncertainty. State-of-the-art planners seek to minimize the localization
uncertainty by only considering the geometric structure of the scene. In this
paper, we argue that motion planning for vision-controlled robots should be
perception aware in that the robot should also favor texture-rich areas to
minimize the localization uncertainty during a goal-reaching task. Thus, we
describe how to optimally incorporate the photometric information (i.e.,
texture) of the scene, in addition to the the geometric one, to compute the
uncertainty of vision-based localization during path planning. To avoid the
caveats of feature-based localization systems (i.e., dependence on feature type
and user-defined thresholds), we use dense, direct methods. This allows us to
compute the localization uncertainty directly from the intensity values of
every pixel in the image. We also describe how to compute trajectories online,
considering also scenarios with no prior knowledge about the map. The proposed
framework is general and can easily be adapted to different robotic platforms
and scenarios. The effectiveness of our approach is demonstrated with extensive
experiments in both simulated and real-world environments using a
vision-controlled micro aerial vehicle.Comment: 16 pages, 20 figures, revised version. Conditionally accepted for
IEEE Transactions on Robotic
Shape basis interpretation for monocular deformable 3D reconstruction
© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In this paper, we propose a novel interpretable shape model to encode object non-rigidity. We first use the initial frames of a monocular video to recover a rest shape, used later to compute a dissimilarity measure based on a distance matrix measurement. Spectral analysis is then applied to this matrix to obtain a reduced shape basis, that in contrast to existing approaches, can be physically interpreted. In turn, these pre-computed shape bases are used to linearly span the deformation of a wide variety of objects. We introduce the low-rank basis into a sequential approach to recover both camera motion and non-rigid shape from the monocular video, by simply optimizing the weights of the linear combination using bundle adjustment. Since the number of parameters to optimize per frame is relatively small, specially when physical priors are considered, our approach is fast and can potentially run in real time. Validation is done in a wide variety of real-world objects, undergoing both inextensible and extensible deformations. Our approach achieves remarkable robustness to artifacts such as noisy and missing measurements and shows an improved performance to competing methods.Peer ReviewedPostprint (author's final draft
Quantum Caustics for Systems with Quadratic Lagrangians
We study caustics in classical and quantum mechanics for systems with
quadratic Lagrangians. We derive a closed form of the transition amplitude on
caustics and discuss their physical implications in the Gaussian slit
(gedanken-)experiment. Application to the quantum mechanical rotor casts doubt
on the validilty of Jevicki's correspondence hypothesis which states that in
quantum mechanics, stationary points(instantons) arise as simple poles.Comment: TeX, 37 pages, 3 figures, Corrected typos and eq.(3.21
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