RoboCup is an international competition that promotes and advances robotics and AI through the challenges presented by its various leagues. We got the chance to sit down with Professor Manuela Veloso, one of RoboCup's founders, to find out more about how it all started, how the community has grown over the years, and the vision for the future. I think it would be very interesting to go right back to the beginning and hear how RoboCup got started. What was the initial idea, and how did it get set up? So we are talking about the mid-90s. In terms of the research in those days, it was the beginning of the internet and many AI and computer science researchers were focused on the internet, first on sophisticated search algorithms, on natural language understanding, on information retrieval, and then on software agents and machine learning applied to digital information. From what I recall, there was a smaller group of researchers who were interested in actual, physical robots, and in particular in AI and robotics.

Major leap towards reanimation after death as mammal's brain preserved A pig's brain has been frozen with its cellular activity locked in place and minimal damage. Could our brains one day be preserved in a way that locks in our thoughts, feelings and perceptions? An entire mammalian brain has been successfully preserved using a technique that will now be offered to people who are terminally ill. The intention is to preserve all the neural information thought necessary to one day reconstruct the mind of the person it once belonged to. "They would need to donate their brain and body for scientific research," says Borys Wróbel at Nectome in San Francisco, California, a research company focused on memory preservation.

Modern discriminative predictors have been shown to match natural intelligences in specific perceptual tasks in image classification, object and part detection, boundary extraction, etc. However, a major advantage that natural intelligences still have is that they work well for all perceptual problems together, solving them efficiently and coherently in an integrated manner. In order to capture some of these advantages in machine perception, we ask two questions: whether deep neural networks can learn universal image representations, useful not only for a single task but for all of them, and how the solutions to the different tasks can be integrated in this framework. We answer by proposing a new architecture, which we call multinet, in which not only deep image features are shared between tasks, but where tasks can interact in a recurrent manner by encoding the results of their analysis in a common shared representation of the data. In this manner, we show that the performance of individual tasks in standard benchmarks can be improved first by sharing features between them and then, more significantly, by integrating their solutions in the common representation.

To promote better performance-bandwidth trade-off for multi-agent perception, weproposeanovel distilledcollaborationgraph (DiscoGraph)tomodeltrainable, pose-aware, and adaptive collaboration among agents. Our key novelties lie in twoaspects.

Motion is a powerful cue that humans use to detect and segment visual objects.

The ability to promptly respond to environmental changes is crucial for the perception system of autonomous driving.