ﻻ يوجد ملخص باللغة العربية
There has been an emerging paradigm shift from the era of internet AI to embodied AI, whereby AI algorithms and agents no longer simply learn from datasets of images, videos or text curated primarily from the internet. Instead, they learn through embodied physical interactions with their environments, whether real or simulated. Consequently, there has been substantial growth in the demand for embodied AI simulators to support a diversity of embodied AI research tasks. This growing interest in embodied AI is beneficial to the greater pursuit of artificial general intelligence, but there is no contemporary and comprehensive survey of this field. This paper comprehensively surveys state-of-the-art embodied AI simulators and research, mapping connections between these. By benchmarking nine state-of-the-art embodied AI simulators in terms of seven features, this paper aims to understand the simulators in their provision for use in embodied AI research. Finally, based upon the simulators and a pyramidal hierarchy of embodied AI research tasks, this paper surveys the main research tasks in embodied AI -- visual exploration, visual navigation and embodied question answering (QA), covering the state-of-the-art approaches, evaluation and datasets.
The domain of Embodied AI, in which agents learn to complete tasks through interaction with their environment from egocentric observations, has experienced substantial growth with the advent of deep reinforcement learning and increased interest from
We describe a framework for research and evaluation in Embodied AI. Our proposal is based on a canonical task: Rearrangement. A standard task can focus the development of new techniques and serve as a source of trained models that can be transferred
Game AI competitions are important to foster research and development on Game AI and AI in general. These competitions supply different challenging problems that can be translated into other contexts, virtual or real. They provide frameworks and tool
A smart city can be seen as a framework, comprised of Information and Communication Technologies (ICT). An intelligent network of connected devices that collect data with their sensors and transmit them using cloud technologies in order to communicat
This paper briefly reviews the history of meta-learning and describes its contribution to general AI. Meta-learning improves model generalization capacity and devises general algorithms applicable to both in-distribution and out-of-distribution tasks