Multivariate Time Series Forecasting focuses on the prediction of future values based on historical context. State-of-the-art sequence-to-sequence models rely on neural attention between timesteps, which allows for temporal learning but fails to consider distinct spatial relationships between variables. In contrast, methods based on graph neural networks explicitly model variable relationships. However, these methods often rely on predefined graphs and perform separate spatial and temporal updates without establishing direct connections between each variable at every timestep. This paper addresses these problems by translating multivariate forecasting into a spatiotemporal sequence formulation where each Transformer input token represents the value of a single variable at a given time. Long-Range Transformers can then learn interactions between space, time, and value information jointly along this extended sequence. Our method, which we call Spacetimeformer, achieves competitive results on benchmarks from traffic forecasting to electricity demand and weather prediction while learning fully-connected spatiotemporal relationships purely from data.
This is a project that ressembles the classic Game of Life, but implemented in a 3D world. The project needs OpenGL 4.5 or greater to render, and currently only compiles under Linux (although it would be relatively easy to change CMakeLists to compile for Windows/macOS*(apple discontinued OpenGL support in favor of Metal)).
Insects typically have a variety of complex exoskeleton structures, which support them in their movements and everyday activities. Fabricating artificial exoskeletons for insect-inspired robots that match the complexity of these naturally-occurring structures is a key challenge in the field of robotics.
Although researchers have proposed several fabrication processes and techniques to produce exoskeletons for insect-inspired robots, many of these methods are extremely complex or rely on expensive equipment and materials. This makes them unfeasible and difficult to apply on a wider scale.
With this in mind, researchers at the University of California in San Diego have recently developed a new process to design and fabricate components for insect-inspired robots with exoskeleton structures. They introduced this process, called flexoskeleton printing, in a paper prepublished on arXiv.
“Inspired by the insect exoskeleton, we present a new fabrication process called ‘flexoskeleton’ printing that enables rapid and accessible fabrication of hybrid rigid/soft robots,” the researchers wrote in their paper.
So far, hybrid robots with both rigid and soft components have been typically built using expensive materials and 3-D printers, as well as multi-step casting and machine processes. In their study, the research team at UC San Diego set out to create a new fabrication method that is cheaper and easier to use.
Flexoskeleton printing, the method they developed, relies on an adaptation of a consumer grade fused deposition material (FDM) 3-D printer, which provides an extremely strong bond strength between the deposited material and the printer’s flexible base layer. This process can be used to create exoskeletons for insect-inspired robots with different shapes and morphologies.
Remarkably, the fabrication approach proposed by the researchers can be used by both novice and expert users, as it is fairly straightforward and easy to understand. It is also far more affordable than alternative fabrication methods, as the materials and equipment it relies on are considerably cheap and readily available.
In their study, the team demonstrated the feasibility of their approach by using it to design and test a wide variety of canonical flexoskeleton elements. They then combined all the elements they produced into a walking four-legged robot with a flexible exoskeleton structure.
“The approach we have developed relies heavily on the interrelationships between three dimensional geometry of surface features and their contributions to the local mechanical properties of that component,” the researchers wrote in their paper. “We envision that this method will enable a new class of bio-inspired robots with focus on the interrelationships between mechanical design and locomotion.”
In the future, the new design and fabrication process devised by this team of researchers could enable the development of numerous insect-inspired robots. As the technique is far more straightforward and affordable than most existing methods, it could also make existing or new robots easier to scale up, increasing their chances of being produced in larger quantities and appearing on the market.
More information: Flexoskeleton printing for versatile insect-inspired robots. arXiv:1911.06897 [cs.RO]. arxiv.org/abs/1911.06897
Imitating the nematode’s nervous system to process information efficiently, this new intelligent system is more robust, more interpretable, and faster to train than current deep neural network architectures with millions of parameters.
Deep Neural Networks And Other Approaches
Researchers are always looking for new ways to build intelligent models. We all know that really deep supervised models work great when we have sufficient data to train them, but one of the hardest things to do is to generalize well and do it efficiently. We can always go deeper, but it has a high computation cost. So as you may already be thinking, there must be another way to make machines intelligent, needing less data or at least fewer layers in our networks.
One of the most complicated tasks that machine learning researchers and engineers are currently working on is self-driving cars. This is a task where every option needs to be covered, and completely stable, to be able to deploy it on our roads. This process of training a self-driving car typically requires many training examples from real humans as well as a really deep neural network able to understand these data and reproduce the human behaviors in any situation ….