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Creating robots that can perform acrobatic movements such as flips or spinning jumps can be highly challenging. Typically, in fact, these robots require sophisticated hardware designs, motion planners and control algorithms.

Researchers at Massachusetts Institute of Technology (MIT) and University of Massachusetts Amherst recently designed a new humanoid robot supported by an actuator-aware kino-dynamic motion planner and a landing controller. This design, presented in a paper pre-published on arXiv, could allow the humanoid robot to perform back flips and other acrobatic movements.

“In this work, we tried to come up with realistic control algorithm to make a real humanoid robot perform acrobatic behavior such as back/front/side-flip, spinning jump, and jump over an obstacle,” Donghyun Kim, one of the researchers who developed the robot’s software and controller, told TechXplore. “To do that, we first experimentally identified the actuator performance and then represent the primary limitations in our motion planner.”

Strong AI will make excellent scientists.

Calculating the most influential scientific equations is no easy task. But these five certainly rank in the top tier.

A research group working at Uppsala University has succeeded in studying ‘translation factors’ – important components of a cell’s protein synthesis machinery – that are several billion years old. By studying these ancient ‘resurrected’ factors, the researchers were able to establish that they had much broader specificities than their present-day, more specialized counterparts.

In order to survive and grow, all cells contain an in-house protein synthesis factory. This consists of ribosomes and associated translation factors that work together to ensure that the complex protein production process runs smoothly. While almost all components of the modern translational machinery are well known, until now scientists did not know how the process evolved.

The new study, published in the journal Molecular Biology and Evolution, took the research group led by Professor Suparna Sanyal of the Department of Cell and Molecular Biology on an epic journey back into the past. A previously published study used a special algorithm to predict DNA sequences of ancestors of an important translation factor called elongation factor thermo-unstable, or EF-Tu, going back billions of years. The Uppsala research group used these DNA sequences to resurrect the ancient bacterial EF-Tu proteins and then to study their properties.

A robotic artist powered by AI algorithms has created realistic self-portraits that question the limits of artificial intelligence and what it means to be human.

Keep watching to look at three of the most fantastic quantum breakthroughs that bring liberation and freedom to the world of science today! Subscribe to Futurity for more videos.

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In 2018, Cornell researchers built a high-powered detector that, in combination with an algorithm-driven process called ptychography, set a world record by tripling the resolution of a state-of-the-art electron microscope.

As successful as it was, that approach had a weakness. It only worked with ultrathin samples that were a few atoms thick. Anything thicker would cause the electrons to scatter in ways that could not be disentangled.

Now a team, again led by David Muller, the Samuel B. Eckert Professor of Engineering, has bested its own record by a factor of two with an electron microscope pixel array detector (EMPAD) that incorporates even more sophisticated 3D reconstruction algorithms.

An international research team analyzed a database of more than 1000 supernova explosions and found that models for the expansion of the Universe best match the data when a new time dependent variation is introduced. If proven correct with future, higher-quality data from the Subaru Telescope and other observatories, these results could indicate still unknown physics working on the cosmic scale.

Edwin Hubble’s observations over 90 years ago showing the expansion of the Universe remain a cornerstone of modern astrophysics. But when you get into the details of calculating how fast the Universe was expanding at different times in its history, scientists have difficulty getting theoretical models to match observations.

To solve this problem, a team led by Maria Dainotti (Assistant Professor at the National Astronomical Observatory of Japan and the Graduate University for Advanced Studies, SOKENDAI in Japan and an affiliated scientist at the Space Science Institute in the U.S.A.) analyzed a catalog of 1048 supernovae which exploded at different times in the history of the Universe. The team found that the theoretical models can be made to match the observations if one of the constants used in the equations, appropriately called the Hubble constant, is allowed to vary with time.

Researchers with the BrainGate Collaboration have deciphered the brain activity associated with handwriting: working with a 65-year-old (at the time of the study) participant with paralysis who has sensors implanted in his brain, they used an algorithm to identify letters as he attempted to write them; then, the system displayed the text on a screen; by attempting handwriting, the participant typed 90 characters per minute — more than double the previous record for typing with a brain-computer interface.

So far, a major focus of brain-computer interface research has been on restoring gross motor skills, such as reaching and grasping or point-and-click typing with a computer cursor.

Machine learning algorithms have gained fame for being able to ferret out relevant information from datasets with many features, such as tables with dozens of rows and images with millions of pixels. Thanks to advances in cloud computing, you can often run very large machine learning models without noticing how much computational power works behind the scenes.

But every new feature that you add to your problem adds to its complexity, making it harder to solve it with machine learning algorithms. Data scientists use dimensionality reduction, a set of techniques that remove excessive and irrelevant features from their machine learning models.

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