3,475 research outputs found
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Icanlearn: A Mobile Application For Creating Flashcards And Social Stories\u3csup\u3etm\u3c/sup\u3e For Children With Autistm
The number of children being diagnosed with Autism Spectrum Disorder (ASD) is on the rise, presenting new challenges for their parents and teachers to overcome. At the same time, mobile computing has been seeping its way into every aspect of our lives in the form of smartphones and tablet computers. It seems only natural to harness the unique medium these devices provide and use it in treatment and intervention for children with autism.
This thesis discusses and evaluates iCanLearn, an iOS flashcard app with enough versatility to construct Social StoriesTM. iCanLearn provides an engaging, individualized learning experience to children with autism on a single device, but the most powerful way to use iCanLearn is by connecting two or more devices together in a teacher-learner relationship. The evaluation results are presented at the end of the thesis
Metrics and benchmarks in human-robot interaction: Recent advances in cognitive robotics
International audienceRobots are having an important growing role in human social life, which requires them to be able to behave appropriately to the context of interaction so as to create a successful long-term human-robot relationship. A major challenge in developing intelligent systems , which could enhance the interactive abilities of robots, is defining clear metrics and benchmarks for the different aspects of human-robot interaction, like human and robot skills and performances, which could facilitate comparing between systems and avoid application-biased evaluations based on particular measures. The point of evaluating robotic systems through metrics and benchmarks, in addition to some recent frameworks and technologies that could endow robots with advanced cognitive and communicative abilities, are discussed in this technical report that covers the outcome of our recent workshop on current advances in cognitive robotics: Towards Intelligent Social Robots-Current Advances in Cognitive Robotics, in conjunction with the 15th IEEE-RAS Humanoids Conference-Seoul-South Korea-2015 (https://intelligent-robots-ws.ensta-paristech.fr/). Additionally, a summary of an interactive discussion session between the workshop participants and the invited speakers about different issues related to cognitive robotics research is reported
Robust Continuous System Integration for Critical Deep-Sea Robot Operations Using Knowledge-Enabled Simulation in the Loop
Deep-sea robot operations demand a high level of safety, efficiency and
reliability. As a consequence, measures within the development stage have to be
implemented to extensively evaluate and benchmark system components ranging
from data acquisition, perception and localization to control. We present an
approach based on high-fidelity simulation that embeds spatial and
environmental conditions from recorded real-world data. This simulation in the
loop (SIL) methodology allows for mitigating the discrepancy between simulation
and real-world conditions, e.g. regarding sensor noise. As a result, this work
provides a platform to thoroughly investigate and benchmark behaviors of system
components concurrently under real and simulated conditions. The conducted
evaluation shows the benefit of the proposed work in tasks related to
perception and self-localization under changing spatial and environmental
conditions.Comment: published on IROS 201
The development of numerical cognition in children and artificial systems: a review of the current knowledge and proposals for multi-disciplinary research
Numerical cognition is a distinctive component of human intelligence such that the observation of
its practice provides a window into high-level brain function. The modelling of numerical abilities in artificial
cognitive systems can help to confirm existing child development hypotheses and define new ones by
means of computational simulations. Meanwhile, new research will help to discover innovative principles
for the design of artificial agents with advanced reasoning capabilities and clarify the underlying algorithms
(e.g. deep learning) that can be highly effective but difficult to understand for humans.
This article promotes new investigation by providing a common resource for researchers with different
backgrounds, including computer science, robotics, neuroscience, psychology, and education, who are
interested in pursuing scientific collaboration on mutually stimulating research on this topic. The article
emphasises the fundamental role of embodiment in the initial development of numerical cognition in
children. This strong relationship with the body motivates the Cognitive Developmental Robotics (CDR)
approach for new research that can (among others) help to standardise data collection and provide open
databases for benchmarking computational models. Furthermore, we discuss the potential application of
robots in classrooms and argue that the CDR approach can be extended to assist educators and favour
mathematical education
Learning Deep Features for Robotic Inference from Physical Interactions
In order to effectively handle multiple tasks that are not pre-defined, a robotic agent needs to automatically map its high-dimensional sensory inputs into useful features. As a solution, feature learning has empirically shown substantial improvements in obtaining representations that are generalizable to different tasks, compared to feature engineering approaches, but it requires a large amount of data and computational capacity. These challenges are specifically relevant in robotics due to the low signal-to-noise ratios inherent to robotic data, and to the cost typically associated with collecting this type of input. In this paper, we propose a deep probabilistic method based on Convolutional Variational Auto-Encoders (CVAEs) to learn visual features suitable for interaction and recognition tasks. We run our experiments on a self-supervised robotic sensorimotor dataset. Our data was acquired with the iCub humanoid and is based on a standard object collection, thus being readily extensible. We evaluated the learned features in terms of usability for 1) object recognition, 2) capturing the statistics of the effects, and 3) planning. In addition, where applicable, we compared the performance of the proposed architecture with other state-ofthe-art models. These experiments demonstrate that our model is capable of capturing the functional statistics of action and perception (i.e. images) which performs better than existing baselines, without requiring millions of samples or any handengineered features
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