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There are some tasks that traditional robots — the rigid and metallic kind — simply aren’t cut out for. Soft-bodied robots, on the other hand, may be able to interact with people more safely or slip into tight spaces with ease. But for robots to reliably complete their programmed duties, they need to know the whereabouts of all their body parts. That’s a tall task for a soft robot that can deform in a virtually infinite number of ways.

MIT researchers have developed an algorithm to help engineers design soft robots that collect more useful information about their surroundings. The deep-learning algorithm suggests an optimized placement of sensors within the robot’s body, allowing it to better interact with its environment and complete assigned tasks. The advance is a step toward the automation of robot design. “The system not only learns a given task, but also how to best design the robot to solve that task,” says Alexander Amini. “Sensor placement is a very difficult problem to solve. So, having this solution is extremely exciting.”

The research will be presented during April’s IEEE International Conference on Soft Robotics and will be published in the journal IEEE Robotics and Automation Letters. Co-lead authors are Amini and Andrew Spielberg, both PhD students in MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). Other co-authors include MIT PhD student Lillian Chin, and professors Wojciech Matusik and Daniela Rus.

Sustainable and responsible lunar services and transportation — yoav landsman, co-founder, moonscape.


Yoav Landsman is the Co-founder of Moonscape (https://www.moonscape.space/), a lunar services and payload transportation company, that is focused on providing necessary services like communication relay and cutting-edge imaging, while delivering payloads to the Moon.

Moonscape’s vision is to support humankind’s effort of reaching the Moon in a sustainable and responsible manner, as humanity’s first step towards the rest of the solar system and beyond.

How recent research points the way towards defeating adversarial examples and achieving a more resilient, consistent and flexible A.I.


How recent neuroscience research points the way towards defeating adversarial examples and achieving a more resilient, consistent and flexible form of artificial intelligence.

For 50 years, the research community has been hunting unsuccessfully for the so-called Odderon particle. Now, a Swedish-Hungarian research group has discovered the mythical particle with the help of extensive analysis of experimental data from the Large Hadron Collider at CERN in Switzerland.

In 1973, two French particle physicists found that, according to their calculations, there was a previously unknown quasi-particle. The discovery sparked an international hunt.

The Odderon particle is what briefly forms when protons collide in high-energy collisions, and in some cases do not shatter, but bounce off one another and scatter. Protons are made up of quarks and gluons, that briefly form Odderon and Pomeron particles.

Texas-based construction company ICON has delivered what it hails as the “world’s first” 3D printed lunar launch and landing pad to NASA, bringing its goal of creating an off-world construction system for the moon a step closer.

Working with a team of students from 10 colleges and universities across the US, ICON used its proprietary technology to 3D print a reusable landing pad using materials found on the moon. The partners recently conducted a static fire test of the rocket pad with a rocket motor at Camp Swift, a Texas Military Department location just outside of Austin.

“Previously reported detection of plant biomagnetism, which established the existence of measurable magnetic activity in the plant kingdom, was carried out using superconducting-quantum-interference-device (SQUID) magnetometers1, 5, 16. Atomic magnetometers are arguably more attractive for biological applications, since, unlike SQUIDs34, 35, they are non-cryogenic and can be miniaturized to optimize spatial resolution of measured biological features14, 15, 36. In the future, the SNR of magnetic measurements in plants will benefit from optimizing the low-frequency stability and sensitivity of atomic magnetometers. Just as noninvasive magnetic techniques have become essential tools for medical diagnostics of the human brain and body, this noninvasive technique could also be useful in the future for crop-plant diagnostics—by measuring the electromagnetic response of plants facing such challenges as sudden temperature change, herbivore attack, and chemical exposure.”


Upon stimulation, plants elicit electrical signals that can travel within a cellular network analogous to the animal nervous system. It is well-known that in the human brain, voltage changes in certain regions result from concerted electrical activity which, in the form of action potentials (APs), travels within nerve-cell arrays. Electro-and magnetophysiological techniques like electroencephalography, magnetoencephalography, and magnetic resonance imaging are used to record this activity and to diagnose disorders. Here we demonstrate that APs in a multicellular plant system produce measurable magnetic fields. Using atomic optically pumped magnetometers, biomagnetism associated with electrical activity in the carnivorous Venus flytrap, Dionaea muscipula, was recorded. Action potentials were induced by heat stimulation and detected both electrically and magnetically.

Interstellar travel has always caught the imagination of humankind. Though our scientific knowledge and imagination have long conceptualised interstellar space travel, actual travel is a massive undertaking. Till date, only two spacecraft (Voyager 1 and 2) have crossed the solar system boundary. More spacecraft will surely follow in future.

The main issue in interstellar travel is that a person aboard the spacecraft sees a different starscape than that from Earth. The astronaut will see position and movement of stars differently than on Earth.