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#438553 New Drone Software Handles Motor ...

Good as some drones are becoming at obstacle avoidance, accidents do still happen. And as far as robots go, drones are very much on the fragile side of things. Any sort of significant contact between a drone and almost anything else usually results in a catastrophic, out-of-control spin followed by a death plunge to the ground. Bad times. Bad, expensive times.

A few years ago, we saw some interesting research into software that can keep the most common drone form factor, the quadrotor, aloft and controllable even after the failure of one motor. The big caveat to that software was that it relied on GPS for state estimation, meaning that without a GPS signal, the drone is unable to get the information it needs to keep itself under control. In a paper recently accepted to RA-L, researchers at the University of Zurich report that they have developed a vision-based system that brings state estimation completely on-board. The upshot: potentially any drone with some software and a camera can keep itself safe even under the most challenging conditions.

A few years ago, we wrote about first author Sihao Sun’s work on high speed controlled flight of a quadrotor with a non-functional motor. But that innovation relied on an external motion capture system. Since then, Sun has moved from Tu Delft to Davide Scaramuzza’s lab at UZH, and it looks like he’s been able to combine his work on controlled spinning flight with the Robotics and Perception Group’s expertise in vision. Now, a downward-facing camera is all it takes for a spinning drone to remain stable and controllable:

Remember, this software isn’t just about guarding against motor failure. Drone motors themselves don’t just up and fail all that often, either with respect to their software or hardware. But they do represent the most likely point of failure for any drone, usually because when you run into something, what ultimately causes your drone to crash is damage to a motor or a propeller that causes loss of control.

The reason that earlier solutions relied on GPS was because the spinning drone needs a method of state estimation—that is, in order to be closed-loop controllable, the drone needs to have a reasonable understanding of what its position is and how that position is changing over time. GPS is an easy way to take care of this, but GPS is also an external system that doesn’t work everywhere. Having a state estimation system that’s completely internal to the drone itself is much more fail safe, and Sun got his onboard system to work through visual feature tracking with a downward-facing camera, even as the drone is spinning at over 20 rad/s.

While the system works well enough with a regular downward-facing camera—something that many consumer drones are equipped with for stabilization purposes—replacing it with an event camera (you remember event cameras, right?) makes the performance even better, especially in low light.

For more details on this, including what you’re supposed to do with a rapidly spinning partially disabled quadrotor (as well as what it’ll take to make this a standard feature on consumer hardware), we spoke with Sihao Sun via email.

IEEE Spectrum: what usually happens when a drone spinning this fast lands? Is there any way to do it safely?

Sihao Sun: Our experience shows that we can safely land the drone while it is spinning. When the range sensor measurements are lower than a threshold (around 10 cm, indicating that the drone is close to the ground), we switch off the rotors. During the landing procedure, despite the fast spinning motion, the thrust direction oscillates around the gravity vector, thus the drone touches the ground with its legs without damaging other components.

Can your system handle more than one motor failure?

Yes, the system can also handle the failure of two opposing rotors. However, if two adjacent rotors or more than two rotors fail, our method cannot save the quadrotor. Some research has shown that it is possible to control a quadrotor with only one remaining rotor. But the drone requires a very special inertial property, which is hard to satisfy in real applications.

How different is your system's performance from a similar system that relies on GPS, in a favorable environment?

In a favorable environment, our system outperforms those relying on GPS signals because it obtains better position estimates. Since a damaged quadrotor spins fast, the accelerometer readings are largely affected by centrifugal forces. When the GPS signal is lost or degraded, a drone relying on GPS needs to integrate these biased accelerometer measurements for position estimation, leading to large position estimation errors. Feeding these erroneous estimates to the flight controller can easily crash the drone.

When you say that your solution requires “only onboard sensors and computation,” are those requirements specialized, or would they be generally compatible with the current generation of recreational and commercial quadrotors?

We use an NVIDIA Jetson TX2 to run our solution, which includes two parts: the control algorithm and the vision-based state estimation algorithm. The control algorithm is lightweight; thus, we believe that it is compatible with the current generation of quadrotors. On the other hand, the vision-based state estimation requires relatively more computational resources, which may not be affordable for cheap recreational platforms. But this is not an issue for commercial quadrotors because many of them have more powerful processors than a TX2.

What else can event cameras be used for, in recreational or commercial applications?

Many drone applications can benefit from event cameras, especially those in high-speed or low-light conditions, such as autonomous drone racing, cave exploration, drone delivery during night time, etc. Event cameras also consume very little power, which is a significant advantage for energy-critical missions, such as planetary aerial vehicles for Mars explorations. Regarding space applications, we are currently collaborating with JPL to explore the use of event cameras to address the key limitations of standard cameras for the next Mars helicopter.

[ UZH RPG ] Continue reading

Posted in Human Robots

#438012 Video Friday: These Robots Have Made 1 ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):

HRI 2021 – March 8-11, 2021 – [Online Conference]
RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
Let us know if you have suggestions for next week, and enjoy today's videos.

We're proud to announce Starship Delivery Robots have now completed 1,000,000 autonomous deliveries around the world. We were unsure where the one millionth delivery was going to take place, as there are around 15-20 service areas open globally, all with robots doing deliveries every minute. In the end it took place at Bowling Green, Ohio, to a student called Annika Keeton who is a freshman studying pre-health Biology at BGSU. Annika is now part of Starship’s history!

[ Starship ]

I adore this little DIY walking robot- with modular feet and little dials to let you easily adjust the walking parameters, it's an affordable kit that's way more nuanced than most.

It's called Bakiwi, and it costs €95. A squee cover made from feathers or fur is an extra €17. Here's a more serious look at what it can do:

[ Bakiwi ]

Thanks Oswald!

Savva Morozov, an AeroAstro junior, works on autonomous navigation for the MIT mini cheetah robot and reflects on the value of a crowded Infinite Corridor.

[ MIT ]

The world's most advanced haptic feedback gloves just got a huge upgrade! HaptX Gloves DK2 achieves a level of realism that other haptic devices can't match. Whether you’re training your workforce, designing a new product, or controlling robots from a distance, HaptX Gloves make it feel real.

They're the only gloves with true-contact haptics, with patented technology that displace your skin the same way a real object would. With 133 points of tactile feedback per hand, for full palm and fingertip coverage. HaptX Gloves DK2 feature the industry's most powerful force feedback, ~2X the strength of other force feedback gloves. They're also the most accurate motion tracking gloves, with 30 tracked degrees of freedom, sub-millimeter precision, no perceivable latency, and no occlusion.

[ HaptX ]

Yardroid is an outdoor robot “guided by computer vision and artificial intelligence” that seems like it can do almost everything.

These are a lot of autonomous capabilities, but so far, we've only seen the video. So, best not to get too excited until we know more about how it works.

[ Yardroid ]

Thanks Dan!

Since as far as we know, Pepper can't spread COVID, it had a busy year.

I somehow missed seeing that chimpanzee magic show, but here it is:

[ Simon Pierro ] via [ SoftBank Robotics ]

In spite of the pandemic, Professor Hod Lipson’s Robotics Studio persevered and even thrived— learning to work on global teams, to develop protocols for sharing blueprints and code, and to test, evaluate, and refine their designs remotely. Equipped with a 3D printer and a kit of electronics prototyping equipment, our students engineered bipedal robots that were conceptualized, fabricated, programmed, and endlessly iterated around the globe in bedrooms, kitchens, backyards, and any other makeshift laboratory you can imagine.

[ Hod Lipson ]

Thanks Fan!

We all know how much quadrupeds love ice!

[ Ghost Robotics ]

We took the opportunity of the last storm to put the Warthog in the snow of Université Laval. Enjoy!

[ Norlab ]

They've got a long way to go, but autonomous indoor firefighting drones seem like a fantastic idea.

[ CTU ]

Individual manipulators are limited by their vertical total load capacity. This places a fundamental limit on the weight of loads that a single manipulator can move. Cooperative manipulation with two arms has the potential to increase the net weight capacity of the overall system. However, it is critical that proper load sharing takes place between the two arms. In this work, we outline a method that utilizes mechanical intelligence in the form of a whiffletree.

And your word of the day is whiffletree, which is “a mechanism to distribute force evenly through linkages.”

[ DART Lab ]

Thanks Raymond!

Some highlights of robotic projects at FZI in 2020, all using ROS.

[ FZI ]

Thanks Fan!

iRobot CEO Colin Angle threatens my job by sharing some cool robots.

[ iRobot ]

A fascinating new talk from Henry Evans on robotic caregivers.

[ HRL ]

The ANA Avatar XPRIZE semifinals selection submission for Team AVATRINA. The setting is a mock clinic, with the patient sitting on a wheelchair and nurse having completed an initial intake. Avatar enters the room controlled by operator (Doctor). A rolling tray table with medical supplies (stethoscope, pulse oximeter, digital thermometer, oxygen mask, oxygen tube) is by the patient’s side. Demonstrates head tracking, stereo vision, fine manipulation, bimanual manipulation, safe impedance control, and navigation.

[ Team AVATRINA ]

This five year old talk from Mikell Taylor, who wrote for us a while back and is now at Amazon Robotics, is entitled “Nobody Cares About Your Robot.” For better or worse, it really doesn't sound like it was written five years ago.

Robotics for the consumer market – Mikell Taylor from Scott Handsaker on Vimeo.

[ Mikell Taylor ]

Fall River Community Media presents this wonderful guy talking about his love of antique robot toys.

If you enjoy this kind of slow media, Fall River also has weekly Hot Dogs Cool Cats adoption profiles that are super relaxing to watch.

[ YouTube ] Continue reading

Posted in Human Robots

#438006 Smellicopter Drone Uses Live Moth ...

Research into robotic sensing has, understandably I guess, been very human-centric. Most of us navigate and experience the world visually and in 3D, so robots tend to get covered with things like cameras and lidar. Touch is important to us, as is sound, so robots are getting pretty good with understanding tactile and auditory information, too. Smell, though? In most cases, smell doesn’t convey nearly as much information for us, so while it hasn’t exactly been ignored in robotics, it certainly isn’t the sensing modality of choice in most cases.

Part of the problem with smell sensing is that we just don’t have a good way of doing it, from a technical perspective. This has been a challenge for a long time, and it’s why we either bribe or trick animals like dogs, rats, vultures, and other animals to be our sensing systems for airborne chemicals. If only they’d do exactly what we wanted them to do all the time, this would be fine, but they don’t, so it’s not.

Until we get better at making chemical sensors, leveraging biology is the best we can do, and what would be ideal would be some sort of robot-animal hybrid cyborg thing. We’ve seen some attempts at remote controlled insects, but as it turns out, you can simplify things if you don’t use the entire insect, but instead just find a way to use its sensing system. Enter the Smellicopter.

There’s honestly not too much to say about the drone itself. It’s an open-source drone project called Crazyflie 2.0, with some additional off the shelf sensors for obstacle avoidance and stabilization. The interesting bits are a couple of passive fins that keep the drone pointed into the wind, and then the sensor, called an electroantennogram.

Image: UW

The drone’s sensor, called an electroantennogram, consists of a “single excised antenna” from a Manduca sexta hawkmoth and a custom signal processing circuit.

To make one of these sensors, you just, uh, “harvest” an antenna from a live hawkmoth. Obligingly, the moth antenna is hollow, meaning that you can stick electrodes up it. Whenever the olfactory neurons in the antenna (which is still technically alive even though it’s not attached to the moth anymore) encounter an odor that they’re looking for, they produce an electrical signal that the electrodes pick up. Plug the other ends of the electrodes into a voltage amplifier and filter, run it through an analog to digital converter, and you’ve got a chemical sensor that weighs just 1.5 gram and consumes only 2.7 mW of power. It’s significantly more sensitive than a conventional metal-oxide odor sensor, in a much smaller and more efficient form factor, making it ideal for drones.

To localize an odor, the Smellicopter uses a simple bioinspired approach called crosswind casting, which involves moving laterally left and right and then forward when an odor is detected. Here’s how it works:

The vehicle takes off to a height of 40 cm and then hovers for ten seconds to allow it time to orient upwind. The smellicopter starts casting left and right crosswind. When a volatile chemical is detected, the smellicopter will surge 25 cm upwind, and then resume casting. As long as the wind direction is fairly consistent, this strategy will bring the insect or robot increasingly closer to a singular source with each surge.

Since odors are airborne, they need a bit of a breeze to spread very far, and the Smellicopter won’t be able to detect them unless it’s downwind of the source. But, that’s just how odors work— even if you’re right next to the source, if the wind is blowing from you towards the source rather than the other way around, you might not catch a whiff of it.

Whenever the olfactory neurons in the antenna encounter an odor that they’re looking for, they produce an electrical signal that the electrodes pick up

There are a few other constraints to keep in mind with this sensor as well. First, rather than detecting something useful (like explosives), it’s going to detect the smells of pretty flowers, because moths like pretty flowers. Second, the antenna will literally go dead on you within a couple hours, since it only functions while its tissues are alive and metaphorically kicking. Interestingly, it may be possible to use CRISPR-based genetic modification to breed moths with antennae that do respond to useful smells, which would be a neat trick, and we asked the researchers—Melanie Anderson, a doctoral student of mechanical engineering at the University of Washington, in Seattle; Thomas Daniel, a UW professor of biology; and Sawyer Fuller, a UW assistant professor of mechanical engineering—about this, along with some other burning questions, via email.

IEEE Spectrum, asking the important questions first: So who came up with “Smellicopter”?

Melanie Anderson: Tom Daniel coined the term “Smellicopter”. Another runner up was “OdorRotor”!

In general, how much better are moths at odor localization than robots?

Melanie Anderson: Moths are excellent at odor detection and odor localization and need to be in order to find mates and food. Their antennae are much more sensitive and specialized than any portable man-made odor sensor. We can't ask the moths how exactly they search for odors so well, but being able to have the odor sensitivity of a moth on a flying platform is a big step in that direction.

Tom Daniel: Our best estimate is that they outperform robotic sensing by at least three orders of magnitude.

How does the localization behavior of the Smellicopter compare to that of a real moth?

Anderson: The cast-and-surge odor search strategy is a simplified version of what we believe the moth (and many other odor searching animals) are doing. It is a reactive strategy that relies on the knowledge that if you detect odor, you can assume that the source is somewhere up-wind of you. When you detect odor, you simply move upwind, and when you lose the odor signal you cast in a cross-wind direction until you regain the signal.

Can you elaborate on the potential for CRISPR to be able to engineer moths for the detection of specific chemicals?

Anderson: CRISPR is already currently being used to modify the odor detection pathways in moth species. It is one of our future efforts to specifically use this to make the antennae sensitive to other chemicals of interest, such as the chemical scent of explosives.

Sawyer Fuller: We think that one of the strengths of using a moth's antenna, in addition to its speed, is that it may provide a path to both high chemical specificity as well as high sensitivity. By expressing a preponderance of only one or a few chemosensors, we are anticipating that a moth antenna will give a strong response only to that chemical. There are several efforts underway in other research groups to make such specific, sensitive chemical detectors. Chemical sensing is an area where biology exceeds man-made systems in terms of efficiency, small size, and sensitivity. So that's why we think that the approach of trying to leverage biological machinery that already exists has some merit.

You mention that the antennae lifespan can be extended for a few days with ice- how feasible do you think this technology is outside of a research context?

Anderson: The antennae can be stored in tiny vials in a standard refrigerator or just with an ice pack to extend their life to about a week. Additionally, the process for attaching the antenna to the electrical circuit is a teachable skill. It is definitely feasible outside of a research context.

Considering the trajectory that sensor development is on, how long do you think that this biological sensor system will outperform conventional alternatives?

Anderson: It's hard to speak toward what will happen in the future, but currently, the moth antenna still stands out among any commercially-available portable sensors.

There have been some experiments with cybernetic insects; what are the advantages and disadvantages of your approach, as opposed to (say) putting some sort of tracking system on a live moth?

Daniel: I was part of a cyber insect team a number of years ago. The challenge of such research is that the animal has natural reactions to attempts to steer or control it.

Anderson: While moths are better at odor tracking than robots currently, the advantage of the drone platform is that we have control over it. We can tell it to constrain the search to a certain area, and return after it finishes searching.

What can you tell us about the health, happiness, and overall wellfare of the moths in your experiments?

Anderson: The moths are cold anesthetized before the antennae are removed. They are then frozen so that they can be used for teaching purposes or in other research efforts.

What are you working on next?

Daniel: The four big efforts are (1) CRISPR modification, (2) experiments aimed at improving the longevity of the antennal preparation, (3) improved measurements of antennal electrical responses to odors combined with machine learning to see if we can classify different odors, and (4) flight in outdoor environments.

Fuller: The moth's antenna sensor gives us a new ability to sense with a much shorter latency than was previously possible with similarly-sized sensors (e.g. semiconductor sensors). What exactly a robot agent should do to best take advantage of this is an open question. In particular, I think the speed may help it to zero in on plume sources in complex environments much more quickly. Think of places like indoor settings with flow down hallways that splits out at doorways, and in industrial settings festooned with pipes and equipment. We know that it is possible to search out and find odors in such scenarios, as anybody who has had to contend with an outbreak of fruit flies can attest. It is also known that these animals respond very quickly to sudden changes in odor that is present in such turbulent, patchy plumes. Since it is hard to reduce such plumes to a simple model, we think that machine learning may provide insights into how to best take advantage of the improved temporal plume information we now have available.

Tom Daniel also points out that the relative simplicity of this project (now that the UW researchers have it all figured out, that is) means that even high school students could potentially get involved in it, even if it’s on a ground robot rather than a drone. All the details are in the paper that was just published in Bioinspiration & Biomimetics. Continue reading

Posted in Human Robots

#437964 How Explainable Artificial Intelligence ...

The field of artificial intelligence has created computers that can drive cars, synthesize chemical compounds, fold proteins, and detect high-energy particles at a superhuman level.

However, these AI algorithms cannot explain the thought processes behind their decisions. A computer that masters protein folding and also tells researchers more about the rules of biology is much more useful than a computer that folds proteins without explanation.

Therefore, AI researchers like me are now turning our efforts toward developing AI algorithms that can explain themselves in a manner that humans can understand. If we can do this, I believe that AI will be able to uncover and teach people new facts about the world that have not yet been discovered, leading to new innovations.

Learning From Experience
One field of AI, called reinforcement learning, studies how computers can learn from their own experiences. In reinforcement learning, an AI explores the world, receiving positive or negative feedback based on its actions.

This approach has led to algorithms that have independently learned to play chess at a superhuman level and prove mathematical theorems without any human guidance. In my work as an AI researcher, I use reinforcement learning to create AI algorithms that learn how to solve puzzles such as the Rubik’s Cube.

Through reinforcement learning, AIs are independently learning to solve problems that even humans struggle to figure out. This has got me and many other researchers thinking less about what AI can learn and more about what humans can learn from AI. A computer that can solve the Rubik’s Cube should be able to teach people how to solve it, too.

Peering Into the Black Box
Unfortunately, the minds of superhuman AIs are currently out of reach to us humans. AIs make terrible teachers and are what we in the computer science world call “black boxes.”

AI simply spits out solutions without giving reasons for its solutions. Computer scientists have been trying for decades to open this black box, and recent research has shown that many AI algorithms actually do think in ways that are similar to humans. For example, a computer trained to recognize animals will learn about different types of eyes and ears and will put this information together to correctly identify the animal.

The effort to open up the black box is called explainable AI. My research group at the AI Institute at the University of South Carolina is interested in developing explainable AI. To accomplish this, we work heavily with the Rubik’s Cube.

The Rubik’s Cube is basically a pathfinding problem: Find a path from point A—a scrambled Rubik’s Cube—to point B—a solved Rubik’s Cube. Other pathfinding problems include navigation, theorem proving and chemical synthesis.

My lab has set up a website where anyone can see how our AI algorithm solves the Rubik’s Cube; however, a person would be hard-pressed to learn how to solve the cube from this website. This is because the computer cannot tell you the logic behind its solutions.

Solutions to the Rubik’s Cube can be broken down into a few generalized steps—the first step, for example, could be to form a cross while the second step could be to put the corner pieces in place. While the Rubik’s Cube itself has over 10 to the 19th power possible combinations, a generalized step-by-step guide is very easy to remember and is applicable in many different scenarios.

Approaching a problem by breaking it down into steps is often the default manner in which people explain things to one another. The Rubik’s Cube naturally fits into this step-by-step framework, which gives us the opportunity to open the black box of our algorithm more easily. Creating AI algorithms that have this ability could allow people to collaborate with AI and break down a wide variety of complex problems into easy-to-understand steps.

A step-by-step refinement approach can make it easier for humans to understand why AIs do the things they do. Forest Agostinelli, CC BY-ND

Collaboration Leads to Innovation
Our process starts with using one’s own intuition to define a step-by-step plan thought to potentially solve a complex problem. The algorithm then looks at each individual step and gives feedback about which steps are possible, which are impossible and ways the plan could be improved. The human then refines the initial plan using the advice from the AI, and the process repeats until the problem is solved. The hope is that the person and the AI will eventually converge to a kind of mutual understanding.

Currently, our algorithm is able to consider a human plan for solving the Rubik’s Cube, suggest improvements to the plan, recognize plans that do not work and find alternatives that do. In doing so, it gives feedback that leads to a step-by-step plan for solving the Rubik’s Cube that a person can understand. Our team’s next step is to build an intuitive interface that will allow our algorithm to teach people how to solve the Rubik’s Cube. Our hope is to generalize this approach to a wide range of pathfinding problems.

People are intuitive in a way unmatched by any AI, but machines are far better in their computational power and algorithmic rigor. This back and forth between man and machine utilizes the strengths from both. I believe this type of collaboration will shed light on previously unsolved problems in everything from chemistry to mathematics, leading to new solutions, intuitions and innovations that may have, otherwise, been out of reach.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

Image Credit: Serg Antonov / Unsplash Continue reading

Posted in Human Robots

#437957 Meet Assembloids, Mini Human Brains With ...

It’s not often that a twitching, snowman-shaped blob of 3D human tissue makes someone’s day.

But when Dr. Sergiu Pasca at Stanford University witnessed the tiny movement, he knew his lab had achieved something special. You see, the blob was evolved from three lab-grown chunks of human tissue: a mini-brain, mini-spinal cord, and mini-muscle. Each individual component, churned to eerie humanoid perfection inside bubbling incubators, is already a work of scientific genius. But Pasca took the extra step, marinating the three components together inside a soup of nutrients.

The result was a bizarre, Lego-like human tissue that replicates the basic circuits behind how we decide to move. Without external prompting, when churned together like ice cream, the three ingredients physically linked up into a fully functional circuit. The 3D mini-brain, through the information highway formed by the artificial spinal cord, was able to make the lab-grown muscle twitch on demand.

In other words, if you think isolated mini-brains—known formally as brain organoids—floating in a jar is creepy, upgrade your nightmares. The next big thing in probing the brain is assembloids—free-floating brain circuits—that now combine brain tissue with an external output.

The end goal isn’t to freak people out. Rather, it’s to recapitulate our nervous system, from input to output, inside the controlled environment of a Petri dish. An autonomous, living brain-spinal cord-muscle entity is an invaluable model for figuring out how our own brains direct the intricate muscle movements that allow us stay upright, walk, or type on a keyboard.

It’s the nexus toward more dexterous brain-machine interfaces, and a model to understand when brain-muscle connections fail—as in devastating conditions like Lou Gehrig’s disease or Parkinson’s, where people slowly lose muscle control due to the gradual death of neurons that control muscle function. Assembloids are a sort of “mini-me,” a workaround for testing potential treatments on a simple “replica” of a person rather than directly on a human.

From Organoids to Assembloids
The miniature snippet of the human nervous system has been a long time in the making.

It all started in 2014, when Dr. Madeleine Lancaster, then a post-doc at Stanford, grew a shockingly intricate 3D replica of human brain tissue inside a whirling incubator. Revolutionarily different than standard cell cultures, which grind up brain tissue to reconstruct as a flat network of cells, Lancaster’s 3D brain organoids were incredibly sophisticated in their recapitulation of the human brain during development. Subsequent studies further solidified their similarity to the developing brain of a fetus—not just in terms of neuron types, but also their connections and structure.

With the finding that these mini-brains sparked with electrical activity, bioethicists increasingly raised red flags that the blobs of human brain tissue—no larger than the size of a pea at most—could harbor the potential to develop a sense of awareness if further matured and with external input and output.

Despite these concerns, brain organoids became an instant hit. Because they’re made of human tissue—often taken from actual human patients and converted into stem-cell-like states—organoids harbor the same genetic makeup as their donors. This makes it possible to study perplexing conditions such as autism, schizophrenia, or other brain disorders in a dish. What’s more, because they’re grown in the lab, it’s possible to genetically edit the mini-brains to test potential genetic culprits in the search for a cure.

Yet mini-brains had an Achilles’ heel: not all were made the same. Rather, depending on the region of the brain that was reverse engineered, the cells had to be persuaded by different cocktails of chemical soups and maintained in isolation. It was a stark contrast to our own developing brains, where regions are connected through highways of neural networks and work in tandem.

Pasca faced the problem head-on. Betting on the brain’s self-assembling capacity, his team hypothesized that it might be possible to grow different mini-brains, each reflecting a different brain region, and have them fuse together into a synchronized band of neuron circuits to process information. Last year, his idea paid off.

In one mind-blowing study, his team grew two separate portions of the brain into blobs, one representing the cortex, the other a deeper part of the brain known to control reward and movement, called the striatum. Shockingly, when put together, the two blobs of human brain tissue fused into a functional couple, automatically establishing neural highways that resulted in one of the most sophisticated recapitulations of a human brain. Pasca crowned this tissue engineering crème-de-la-crème “assembloids,” a portmanteau between “assemble” and “organoids.”

“We have demonstrated that regionalized brain spheroids can be put together to form fused structures called brain assembloids,” said Pasca at the time.” [They] can then be used to investigate developmental processes that were previously inaccessible.”

And if that’s possible for wiring up a lab-grown brain, why wouldn’t it work for larger neural circuits?

Assembloids, Assemble
The new study is the fruition of that idea.

The team started with human skin cells, scraped off of eight healthy people, and transformed them into a stem-cell-like state, called iPSCs. These cells have long been touted as the breakthrough for personalized medical treatment, before each reflects the genetic makeup of its original host.

Using two separate cocktails, the team then generated mini-brains and mini-spinal cords using these iPSCs. The two components were placed together “in close proximity” for three days inside a lab incubator, gently floating around each other in an intricate dance. To the team’s surprise, under the microscope using tracers that glow in the dark, they saw highways of branches extending from one organoid to the other like arms in a tight embrace. When stimulated with electricity, the links fired up, suggesting that the connections weren’t just for show—they’re capable of transmitting information.

“We made the parts,” said Pasca, “but they knew how to put themselves together.”

Then came the ménage à trois. Once the mini-brain and spinal cord formed their double-decker ice cream scoop, the team overlaid them onto a layer of muscle cells—cultured separately into a human-like muscular structure. The end result was a somewhat bizarre and silly-looking snowman, made of three oddly-shaped spherical balls.

Yet against all odds, the brain-spinal cord assembly reached out to the lab-grown muscle. Using a variety of tools, including measuring muscle contraction, the team found that this utterly Frankenstein-like snowman was able to make the muscle component contract—in a way similar to how our muscles twitch when needed.

“Skeletal muscle doesn’t usually contract on its own,” said Pasca. “Seeing that first twitch in a lab dish immediately after cortical stimulation is something that’s not soon forgotten.”

When tested for longevity, the contraption lasted for up to 10 weeks without any sort of breakdown. Far from a one-shot wonder, the isolated circuit worked even better the longer each component was connected.

Pasca isn’t the first to give mini-brains an output channel. Last year, the queen of brain organoids, Lancaster, chopped up mature mini-brains into slices, which were then linked to muscle tissue through a cultured spinal cord. Assembloids are a step up, showing that it’s possible to automatically sew multiple nerve-linked structures together, such as brain and muscle, sans slicing.

The question is what happens when these assembloids become more sophisticated, edging ever closer to the inherent wiring that powers our movements. Pasca’s study targets outputs, but what about inputs? Can we wire input channels, such as retinal cells, to mini-brains that have a rudimentary visual cortex to process those examples? Learning, after all, depends on examples of our world, which are processed inside computational circuits and delivered as outputs—potentially, muscle contractions.

To be clear, few would argue that today’s mini-brains are capable of any sort of consciousness or awareness. But as mini-brains get increasingly more sophisticated, at what point can we consider them a sort of AI, capable of computation or even something that mimics thought? We don’t yet have an answer—but the debates are on.

Image Credit: christitzeimaging.com / Shutterstock.com Continue reading

Posted in Human Robots