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A team of researchers affiliated with a host of institutions in the Republic of Korea has developed a tiny, soft robotic hand that can grasp small objects and measure their temperature. They have published their results in the journal Science Robotics. Continue reading
Most animals are limited to either walking, flying, or swimming, with a handful of lucky species whose physiology allows them to cross over. A new robot took inspiration from them, and can fly like a bird just as well as it can walk like a (weirdly awkward, metallic, tiny) person. It also happens to be able to skateboard and slackline, two skills most humans will never pick up.
Described in a paper published this week in Science Robotics, the robot’s name is Leo, which is short for Leonardo, which is short for LEgs ONboARD drOne. The name makes it sound like a drone with legs, but it has a somewhat humanoid shape, with multi-joint legs, propeller thrusters that look like arms, a “body” that contains its motors and electronics, and a dome-shaped protection helmet.
Leo was built by a team at Caltech, and they were particularly interested in how the robot would transition between walking and flying. The team notes that they studied the way birds use their legs to generate thrust when they take off, and applied similar principles to the robot. In a video that shows Leo approaching a staircase, taking off, and gliding over the stairs to land near the bottom, the robot’s motions are seamlessly graceful.
“There is a similarity between how a human wearing a jet suit controls their legs and feet when landing or taking off and how LEO uses synchronized control of distributed propeller-based thrusters and leg joints,” said Soon-Jo Chung, one of the paper’s authors a professor at Caltech. “We wanted to study the interface of walking and flying from the dynamics and control standpoint.”
Leo walks at a speed of 20 centimeters (7.87 inches) per second, but can move faster by mixing in some flying with the walking. How wide our steps are, where we place our feet, and where our torsos are in relation to our legs all help us balance when we walk. The robot uses its propellers to help it balance, while its leg actuators move it forward.
To teach the robot to slackline—which is much harder than walking on a balance beam—the team overrode its feet contact sensors with a fixed virtual foot contact centered just underneath it, because the sensors weren’t able to detect the line. The propellers played a big part as well, helping keep Leo upright and balanced.
For the robot to ride a skateboard, the team broke the process down into two distinct components: controlling the steering angle and controlling the skateboard’s acceleration and deceleration. Placing Leo’s legs in specific spots on the board made it tilt to enable steering, and forward acceleration was achieved by moving the bot’s center of mass backward while pitching the body forward at the same time.
So besides being cool (and a little creepy), what’s the goal of developing a robot like Leo? The paper authors see robots like Leo enabling a range of robotic missions that couldn’t be carried out by ground or aerial robots.
“Perhaps the most well-suited applications for Leo would be the ones that involve physical interactions with structures at a high altitude, which are usually dangerous for human workers and call for a substitution by robotic workers,” the paper’s authors said. Examples could include high-voltage line inspection, painting tall bridges or other high-up surfaces, inspecting building roofs or oil refinery pipes, or landing sensitive equipment on an extraterrestrial object.
Next up for Leo is an upgrade to its performance via a more rigid leg design, which will help support the robot’s weight and increase the thrust force of its propellers. The team also wants to make Leo more autonomous, and plans to add a drone landing control algorithm to its software, ultimately aiming for the robot to be able to decide where and when to walk versus fly.
Leo hasn’t quite achieved the wow factor of Boston Dynamics’ dancing robots (or its Atlas that can do parkour), but it’s on its way.
Image Credit: Caltech Center for Autonomous Systems and Technologies/Science Robotics Continue reading
Using computer simulations to design new chips played a crucial role in the rapid improvements in processor performance we’ve experienced in recent decades. Now Chinese researchers have extended the approach to the quantum world.
Electronic design automation tools started to become commonplace in the early 1980s as the complexity of processors rose exponentially, and today they are an indispensable tool for chip designers.
More recently, Google has been turbocharging the approach by using artificial intelligence to design the next generation of its AI chips. This holds the promise of setting off a process of recursive self-improvement that could lead to rapid performance gains for AI.
Now, New Scientist has reported on a team from the University of Science and Technology of China in Shanghai that has applied the same ideas to another emerging field of computing: quantum processors. In a paper posted to the arXiv pre-print server, the researchers describe how they used a quantum computer to design a new type of qubit that significantly outperformed their previous design.
“Simulations of high-complexity quantum systems, which are intractable for classical computers, can be efficiently done with quantum computers,” the authors wrote. “Our work opens the way to designing advanced quantum processors using existing quantum computing resources.”
At the heart of the idea is the fact that the complexity of quantum systems grows exponentially as they increase in size. As a result, even the most powerful supercomputers struggle to simulate fairly small quantum systems.
This was the basis for Google’s groundbreaking display of “quantum supremacy” in 2019. The company’s researchers used a 53-qubit processor to run a random quantum circuit a million times and showed that it would take roughly 10,000 years to simulate the experiment on the world’s fastest supercomputer.
This means that using classical computers to help in the design of new quantum computers is likely to hit fundamental limits pretty quickly. Using a quantum computer, however, sidesteps the problem because it can exploit the same oddities of the quantum world that make the problem complex in the first place.
This is exactly what the Chinese researchers did. They used an algorithm called a variational quantum eigensolver to simulate the kind of superconducting electronic circuit found at the heart of a quantum computer. This was used to explore what happens when certain energy levels in the circuit are altered.
Normally this kind of experiment would require them to build large numbers of physical prototypes and test them, but instead the team was able to rapidly model the impact of the changes. The upshot was that the researchers discovered a new type of qubit that was more powerful than the one they were already using.
Any two-level quantum system can act as a qubit, but most superconducting quantum computers use transmons, which encode quantum states into the oscillations of electrons. By tweaking the energy levels of their simulated quantum circuit, the researchers were able to discover a new qubit design they dubbed a plasonium.
It is less than half the size of a transmon, and when the researchers fabricated it they found that it holds its quantum state for longer and is less prone to errors. It still works on similar principles to the transmon, so it’s possible to manipulate it using the same control technologies.
The researchers point out that this is only a first prototype, so with further optimization and the integration of recent progress in new superconducting materials and surface treatment methods they expect performance to increase even more.
But the new qubit the researchers have designed is probably not their most significant contribution. By demonstrating that even today’s rudimentary quantum computers can help design future devices, they’ve opened the door to a virtuous cycle that could significantly speed innovation in this field.
Image Credit: Pete Linforth from Pixabay Continue reading
Lots of robots use bioinspiration in their design. Humanoids, quadrupeds, snake robots—if an animal has figured out a clever way of doing something, odds are there's a robot that's tried to duplicate it. But animals are often just a little too clever for the robots that we build that try to mimic them, which is why researchers at
Swiss Federal Institute of Technology Lausanne in Switzerland (EPFL) are using robots to learn about how animals themselves do what they do. In a paper published today in Science Robotics, roboticists from EPFL's Biorobotics Laboratory introduce a robotic eel that leverages sensory feedback from the water it swims through to coordinate its motion without the need for central control, suggesting a path towards simpler, more robust mobile robots.
The robotic eel—called AgnathaX—is a descendant of
AmphiBot, which has been swimming around at EPFL for something like two decades. AmphiBot's elegant motion in the water has come from the equivalent what are called central pattern generators (CPGs), which are sequences of neural circuits (the biological kind) that generate the sort of rhythms that you see in eel-like animals that rely on oscillations to move. It's possible to replicate these biological circuits using newfangled electronic circuits and software, leading to the same kind of smooth (albeit robotic) motion in AmphiBot.
Biological researchers had pretty much decided that CPGs explained the extent of wiggly animal motion, until it was discovered you can chop an eel's spinal cord in half, and it'll somehow maintain its coordinated undulatory swimming performance. Which is kinda nuts, right? Obviously, something else must be going on, but trying to futz with eels to figure out exactly what it was isn't, I would guess, pleasant for either researchers or their test subjects, which is where the robots come in. We can't make robotic eels that are exactly like the real thing, but we can duplicate some of their sensing and control systems well enough to understand how they do what they do.
AgnathaX exhibits the same smooth motions as the original version of AmphiBot, but it does so without having to rely on centralized programming that would be the equivalent of a biological CPG. Instead, it uses skin sensors that can detect pressure changes in the water around it, a feature also found on actual eels. By hooking these pressure sensors up to AgnathaX's motorized segments, the robot can generate swimming motions even if its segments aren't connected with each other—without a centralized nervous system, in other words. This spontaneous syncing up of disconnected moving elements is called entrainment, and the best demo of it that I've seen is this one, using metronomes:
The reason why this isn't just neat but also useful is that it provides a secondary method of control for robots. If the centralized control system of your swimming robot gets busted, you can rely on this water pressure-mediated local control to generate a swimming motion. And there are applications for modular robots as well, since you can potentially create a swimming robot out of a bunch of different physically connected modules that don't even have to talk to each other.
For more details, we spoke with
Robin Thandiackal and Kamilo Melo at EPFL, first authors on the Science Robotics paper.
IEEE Spectrum: Why do you need a robot to do this kind of research?
Thandiackal and Melo: From a more general perspective, with this kind of research we learn and understand how a system works by building it. This then allows us to modify and investigate the different components and understand their contribution to the system as a whole.
In a more specific context, it is difficult to separate the different components of the nervous system with respect to locomotion within a live animal. The central components are especially difficult to remove, and this is where a robot or also a simulated model becomes useful. We used both in our study. The robot has the unique advantage of using it within the real physics of the water, whereas these dynamics are approximated in simulation. However, we are confident in our simulations too because we validated them against the robot.
How is the robot model likely to be different from real animals? What can't you figure out using the robot, and how much could the robot be upgraded to fill that gap?
Thandiackal and Melo: The robot is by no means an exact copy of a real animal, only a first approximation. Instead, from observing and previous knowledge of real animals, we were able to create a mathematical representation of the neuromechanical control in real animals, and we implemented this mathematical representation of locomotion control on the robot to create a model. As the robot interacts with the real physics of undulatory swimming, we put a great effort in informing our design with the morphological and physiological characteristics of the real animal. This for example accounts for the scaling, the morphology and aspect ratio of the robot with respect to undulatory animals, and the muscle model that we used to approximately represent the viscoelastic characteristics of real muscles with a rotational joint.
Upgrading the robot is not going to be making it more “biological.” Again, the robot is part of the model, not a copy of the real biology. For the sake of this project, the robot was sufficient, and only a few things were missing in our design. You can even add other types of sensors and use the same robot base. However, if we would like to improve our robot for the future, it would be interesting to collect other fluid information like the surrounding fluid speed simultaneously with the force sensing, or to measure hydrodynamic pressure directly. Finally, we aim to test our model of undulatory swimming using a robot with three-dimensional capabilities, something which we are currently working on.
Upgrading the robot is not going to be making it more “biological.” The robot is part of the model, not a copy of the real biology.
What aspects of the function of a nervous system to generate undulatory motion in water aren't redundant with the force feedback from motion that you describe?
Thandiackal and Melo: Apart from the generation of oscillations and intersegmental coupling, which we found can be redundantly generated by the force feedback, the central nervous system still provides unique higher level commands like steering to regulate swimming direction. These commands typically originate in the brain (supraspinal) and are at the same time influenced by sensory signals. In many fish the lateral line organ, which directly connects to the brain, helps to inform the brain, e.g., to maintain position (rheotaxis) under variable flow conditions.
How can this work lead to robots that are more resilient?
Thandiackal and Melo: Robots that have our complete control architecture, with both peripheral and central components, are remarkably fault-tolerant and robust against damage in their sensors, communication buses, and control circuits. In principle, the robot should have the same fault-tolerance as demonstrated in simulation, with the ability to swim despite missing sensors, broken communication bus, or broken local microcontroller. Our control architecture offers very graceful degradation of swimming ability (as opposed to catastrophic failure).
Why is this discovery potentially important for modular robots?
Thandiackal and Melo: We showed that undulatory swimming can emerge in a self-organized manner by incorporating local force feedback without explicit communication between modules. In principle, we could create swimming robots of different sizes by simply attaching independent modules in a chain (e.g., without a communication bus between them). This can be useful for the design of modular swimming units with a high degree of reconfigurability and robustness, e.g. for search and rescue missions or environmental monitoring. Furthermore, the custom-designed sensing units provide a new way of accurate force sensing in water along the entirety of the body. We therefore hope that such units can help swimming robots to navigate through flow perturbations and enable advanced maneuvers in unsteady flows. Continue reading