Over the next few decades, robots could be introduced into human environments, including homes, offices and retail spaces. Among other things, robotic systems could be used to tidy up spaces and make them safer for humans.
Computer Sciences
NeatNet: A model that can learn people’s tidying up preferences
As robots become increasingly advanced and affordable, more people could start introducing them into their homes. Many roboticists have thus been trying to develop systems that can effectively assist humans with house chores, such as cooking, cleaning and tidying up.
A new model that automatically generates movie trailers
Trailers, short video clips that introduce new movies, are often crucial elements in the promotional strategies employed by film production companies. To be most effective, trailers should briefly summarize a movie's plot, conveying its artistic style and overall mood...
A deep learning method to automatically enhance dog animations
Researchers at Trinity College Dublin and University of Bath have recently developed a model based on deep neural networks that could help to improve the quality of animations containing quadruped animals, such as dogs. The framework they created was presented at the...
A Q-learning algorithm to generate shots for walking robots in soccer simulations
RoboCup, originally named the J-League, is an annual robotics and artificial intelligence (AI) competition organized by the International RoboCup Federation. During RoboCup, robots compete with other robots soccer tournaments.
A soft magnetic pixel robot that can be programmed to take different shapes
Magnetic soft robots are systems that can change shape or perform different actions when a magnetic field is applied to them. These robots have numerous advantageous characteristics, including a wireless drive, high flexibility and infinite endurance.
A deep learning technique for global field reconstruction with sparse sensors
Developing methods to accurately reconstruct spatial fields using data collected by sparse sensors has been a long-standing challenge in both physics and computer science. Ultimately, such methods could significantly aid the design, prediction, analysis and control of...
A neural network-based optimization technique inspired by the principle of annealing
Optimization problems involve the identification of the best possible solution among several possibilities. These problems can be encountered in real-world settings, as well as in most scientific research fields.
A technique that allows robots to detect when humans need help
As robots are introduced in an increasing number of real-world settings, it is important for them to be able to effectively cooperate with human users. In addition to communicating with humans and assisting them in everyday tasks, it might thus be useful for robots to...
MERLIN: A self-supervised strategy to train deep despeckling networks
When a highly coherent light beam, such as that emitted by radars, is diffusely reflected on a surface with a rough structure (e.g., a piece of paper, white paint or a metallic surface), it produces a random granular effect known as the 'speckle' pattern. This effect...