739,371 research outputs found
Learning and Acting in Peripersonal Space: Moving, Reaching, and Grasping
The young infant explores its body, its sensorimotor system, and the
immediately accessible parts of its environment, over the course of a few
months creating a model of peripersonal space useful for reaching and grasping
objects around it. Drawing on constraints from the empirical literature on
infant behavior, we present a preliminary computational model of this learning
process, implemented and evaluated on a physical robot. The learning agent
explores the relationship between the configuration space of the arm, sensing
joint angles through proprioception, and its visual perceptions of the hand and
grippers. The resulting knowledge is represented as the peripersonal space
(PPS) graph, where nodes represent states of the arm, edges represent safe
movements, and paths represent safe trajectories from one pose to another. In
our model, the learning process is driven by intrinsic motivation. When
repeatedly performing an action, the agent learns the typical result, but also
detects unusual outcomes, and is motivated to learn how to make those unusual
results reliable. Arm motions typically leave the static background unchanged,
but occasionally bump an object, changing its static position. The reach action
is learned as a reliable way to bump and move an object in the environment.
Similarly, once a reliable reach action is learned, it typically makes a
quasi-static change in the environment, moving an object from one static
position to another. The unusual outcome is that the object is accidentally
grasped (thanks to the innate Palmar reflex), and thereafter moves dynamically
with the hand. Learning to make grasps reliable is more complex than for
reaches, but we demonstrate significant progress. Our current results are steps
toward autonomous sensorimotor learning of motion, reaching, and grasping in
peripersonal space, based on unguided exploration and intrinsic motivation.Comment: 35 pages, 13 figure
Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping
The lack of reliable data in developing countries is a major obstacle to
sustainable development, food security, and disaster relief. Poverty data, for
example, is typically scarce, sparse in coverage, and labor-intensive to
obtain. Remote sensing data such as high-resolution satellite imagery, on the
other hand, is becoming increasingly available and inexpensive. Unfortunately,
such data is highly unstructured and currently no techniques exist to
automatically extract useful insights to inform policy decisions and help
direct humanitarian efforts. We propose a novel machine learning approach to
extract large-scale socioeconomic indicators from high-resolution satellite
imagery. The main challenge is that training data is very scarce, making it
difficult to apply modern techniques such as Convolutional Neural Networks
(CNN). We therefore propose a transfer learning approach where nighttime light
intensities are used as a data-rich proxy. We train a fully convolutional CNN
model to predict nighttime lights from daytime imagery, simultaneously learning
features that are useful for poverty prediction. The model learns filters
identifying different terrains and man-made structures, including roads,
buildings, and farmlands, without any supervision beyond nighttime lights. We
demonstrate that these learned features are highly informative for poverty
mapping, even approaching the predictive performance of survey data collected
in the field.Comment: In Proc. 30th AAAI Conference on Artificial Intelligenc
Quantifying Uncertainties in Natural Language Processing Tasks
Reliable uncertainty quantification is a first step towards building
explainable, transparent, and accountable artificial intelligent systems.
Recent progress in Bayesian deep learning has made such quantification
realizable. In this paper, we propose novel methods to study the benefits of
characterizing model and data uncertainties for natural language processing
(NLP) tasks. With empirical experiments on sentiment analysis, named entity
recognition, and language modeling using convolutional and recurrent neural
network models, we show that explicitly modeling uncertainties is not only
necessary to measure output confidence levels, but also useful at enhancing
model performances in various NLP tasks.Comment: To appear at AAAI 201
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