7,069 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
Unsupervised Discovery of Parts, Structure, and Dynamics
Humans easily recognize object parts and their hierarchical structure by
watching how they move; they can then predict how each part moves in the
future. In this paper, we propose a novel formulation that simultaneously
learns a hierarchical, disentangled object representation and a dynamics model
for object parts from unlabeled videos. Our Parts, Structure, and Dynamics
(PSD) model learns to, first, recognize the object parts via a layered image
representation; second, predict hierarchy via a structural descriptor that
composes low-level concepts into a hierarchical structure; and third, model the
system dynamics by predicting the future. Experiments on multiple real and
synthetic datasets demonstrate that our PSD model works well on all three
tasks: segmenting object parts, building their hierarchical structure, and
capturing their motion distributions.Comment: ICLR 2019. The first two authors contributed equally to this wor
Psychophysical identity and free energy
An approach to implementing variational Bayesian inference in biological
systems is considered, under which the thermodynamic free energy of a system
directly encodes its variational free energy. In the case of the brain, this
assumption places constraints on the neuronal encoding of generative and
recognition densities, in particular requiring a stochastic population code.
The resulting relationship between thermodynamic and variational free energies
is prefigured in mind-brain identity theses in philosophy and in the Gestalt
hypothesis of psychophysical isomorphism.Comment: 22 pages; published as a research article on 8/5/2020 in Journal of
the Royal Society Interfac
Visual Dynamics: Stochastic Future Generation via Layered Cross Convolutional Networks
We study the problem of synthesizing a number of likely future frames from a
single input image. In contrast to traditional methods that have tackled this
problem in a deterministic or non-parametric way, we propose to model future
frames in a probabilistic manner. Our probabilistic model makes it possible for
us to sample and synthesize many possible future frames from a single input
image. To synthesize realistic movement of objects, we propose a novel network
structure, namely a Cross Convolutional Network; this network encodes image and
motion information as feature maps and convolutional kernels, respectively. In
experiments, our model performs well on synthetic data, such as 2D shapes and
animated game sprites, and on real-world video frames. We present analyses of
the learned network representations, showing it is implicitly learning a
compact encoding of object appearance and motion. We also demonstrate a few of
its applications, including visual analogy-making and video extrapolation.Comment: Journal preprint of arXiv:1607.02586 (IEEE TPAMI, 2019). The first
two authors contributed equally to this work. Project page:
http://visualdynamics.csail.mit.ed
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