Lifeboat Foundation

Credits: clone incorporated.

In a paper published on February 23, 2022 in Nature Machine Intelligence, a team of scientists at the Max Planck Institute for Intelligent Systems (MPI-IS) introduce a robust soft haptic sensor named “Insight” that uses computer vision and a deep neural network to accurately estimate where objects come into contact with the sensor and how large the applied forces are. The research project is a significant step toward robots being able to feel their environment as accurately as humans and animals. Like its natural counterpart, the fingertip sensor is very sensitive, robust, and high-resolution.

The thumb-shaped sensor is made of a soft shell built around a lightweight stiff skeleton. This skeleton holds up the structure much like bones stabilize the soft finger tissue. The shell is made from an elastomer mixed with dark but reflective aluminum flakes, resulting in an opaque grayish color that prevents any external light finding its way in. Hidden inside this finger-sized cap is a tiny 160-degree fish-eye camera, which records colorful images, illuminated by a ring of LEDs.

Continue reading “Team develops fingertip sensitivity for robots” »

Identifying a malfunction in the nation’s power grid can be like trying to find a needle in an enormous haystack. Hundreds of thousands of interrelated sensors spread across the U.S. capture data on electric current, voltage, and other critical information in real time, often taking multiple recordings per second.

Researchers at the MIT-IBM Watson AI Lab have devised a computationally efficient method that can automatically pinpoint anomalies in those data streams in real time. They demonstrated that their artificial intelligence method, which learns to model the interconnectedness of the power grid, is much better at detecting these glitches than some other popular techniques.

Because the machine-learning model they developed does not require annotated data on power grid anomalies for training, it would be easier to apply in real-world situations where high-quality labeled datasets are often hard to come by. The model is also flexible and can be applied to other situations where a vast number of interconnected sensors collect and report data, like traffic monitoring systems. It could, for example, identify traffic bottlenecks or reveal how traffic jams cascade.

A new study challenges the conventional approach to designing soft robotics and a class of materials called metamaterials by utilizing the power of computer algorithms. Researchers from the University of Illinois Urbana-Champaign and Technical University of Denmark can now build multimaterial structures without dependence on human intuition or trial-and-error to produce highly efficient actuators and energy absorbers that mimic designs found in nature.

The study, led by Illinois civil and environmental engineering professor Shelly Zhang, uses optimization theory and an algorithm-based design process called topology optimization. Also known as digital synthesis, the design process builds composite structures that can precisely achieve complex prescribed mechanical responses.

Continue reading “New approach to flexible robotics and metamaterials design mimics nature, encourages sustainability” »

Additive manufacturing, or 3D printing, can create custom parts for electromagnetic devices on-demand and at a low cost. These devices are highly sensitive, and each component requires precise fabrication. Until recently, though, the only way to diagnose printing errors was to make, measure and test a device or to use in-line simulation, both of which are computationally expensive and inefficient.

To remedy this, a research team co-led by Penn State created a first-of-its-kind methodology for diagnosing printing errors with machine learning in real time. The researchers describe this framework—published in Additive Manufacturing —as a critical first step toward correcting 3D-printing errors in real time. According to the researchers, this could make printing for sensitive devices much more effective in terms of time, cost and computational bandwidth.

“A lot of things can go wrong during the additive manufacturing process for any component,” said Greg Huff, associate professor of electrical engineering at Penn State. “And in the world of electromagnetics, where dimensions are based on wavelengths rather than regular units of measure, any small defect can really contribute to large-scale system failures or degraded operations. If 3D printing a household item is like tuning a tuba—which can be done with broad adjustments—3D-printing devices functioning in the electromagnetic domain is like tuning a violin: Small adjustments really matter.”

Engineers sometimes turn to nature for inspiration. Cold Spring Harbor Laboratory Associate Professor Saket Navlakha and research scientist Jonathan Suen have found that adjustment algorithms—the same feedback control process by which the Internet optimizes data traffic—are used by several natural systems to sense and stabilize behavior, including ant colonies, cells, and neurons.

Internet engineers route data around the world in small packets, which are analogous to ants. As Navlakha explains, “The goal of this work was to bring together ideas from machine learning and Internet design and relate them to the way ant colonies forage.”

The same algorithm used by internet engineers is used by ants when they forage for food. At first, the colony may send out a single ant. When the ant returns, it provides information about how much food it got and how long it took to get it. The colony would then send out two ants. If they return with food, the colony may send out three, then four, five, and so on. But if ten ants are sent out and most do not return, then the colony does not decrease the number it sends to nine. Instead, it cuts the number by a large amount, a multiple (say half) of what it sent before: only five ants. In other words, the number of ants slowly adds up when the signals are positive, but is cut dramatically lower when the information is negative. Navlakha and Suen note that the system works even if individual ants get lost and parallels a particular type of “additive-increase/multiplicative-decrease algorithm” used on the internet.

A series of experiments using paper airplanes reveals new aerodynamic effects, a team of scientists has discovered. Its findings enhance our understanding of flight stability and could inspire new types of flying robots and small drones.

“The study started with simple curiosity about what makes a good paper airplane and specifically what is needed for smooth gliding,” explains Leif Ristroph, an associate professor at New York University’s Courant Institute of Mathematical Sciences and an author of the study, which appears in the Journal of Fluid Mechanics. “Answering such basic questions ended up being far from child’s play. We discovered that the aerodynamics of how paper airplanes keep level flight is really very different from the stability of conventional airplanes.”

“Birds glide and soar in an effortless way, and paper airplanes, when tuned properly, can also glide for long distances,” adds author Jane Wang, a professor of engineering and physics at Cornell University. “Surprisingly, there has been no good mathematical model for predicting this seemingly simple but subtle gliding flight.”

The content of Peter’s email blast has been edited by me (can’t help myself). But I believe I have captured its essence and hope you enjoy the retelling. As always your comments are welcomed.

What is Insilico Medicine?

Insilico Medicine is a pioneering drug company that is powered by a “drug discovery engine” that sifts through millions of data samples to determine the signature biological characteristics of specific diseases. It then identifies the most promising treatment targets and uses a new AI technique called generative adversarial networks (GANs) to create molecules perfectly suited against them.

Elon Musk announces that Tesla will soon be joining the robotic industry. New release of cars in 2022 will be delayed to make way for the humanoid robot development of the company.