Tag Archives: design
#437800 Malleable Structure Makes Robot Arm More ...
The majority of robot arms are built out of some combination of long straight tubes and actuated joints. This isn’t surprising, since our limbs are built the same way, which was a clever and efficient bit of design. By adding more tubes and joints (or degrees of freedom), you can increase the versatility of your robot arm, but the tradeoff is that complexity, weight, and cost will increase, too.
At ICRA, researchers from Imperial College London’s REDS Lab, headed by Nicolas Rojas, introduced a design for a robot that’s built around a malleable structure rather than a rigid one, allowing you to improve how versatile the arm is without having to add extra degrees of freedom. The idea is that you’re no longer constrained to static tubes and joints but can instead reconfigure your robot to set it up exactly the way you want and easily change it whenever you feel like.
Inside of that bendable section of arm are layers and layers of mylar sheets, cut into flaps and stacked on top of one another so that each flap is overlapping or overlapped by at least 11 other flaps. The mylar is slippery enough that under most circumstances, the flaps can move smoothly against each other, letting you adjust the shape of the arm. The flaps are sealed up between latex membranes, and when air is pumped out from between the membranes, they press down on each other and turn the whole structure rigid, locking itself in whatever shape you’ve put it in.
Image: Imperial College London
The malleable part of the robot consists of layers of mylar sheets, cut into flaps that can move smoothly against each other, letting you adjust the shape of the arm. The flaps are sealed up between latex membranes, and when air is pumped out from between the membranes, they press down on each other and turn the whole structure rigid, locking itself in whatever shape you’ve put it in.
The nice thing about this system is that it’s a sort of combination of a soft robot and a rigid robot—you get the flexibility (both physical and metaphorical) of a soft system, without necessarily having to deal with all of the control problems. It’s more mechanically complex than either (as hybrid systems tend to be), but you save on cost, size, and weight, and reduce the number of actuators you need, which tend to be points of failure. You do need to deal with creating and maintaining a vacuum, and the fact that the malleable arm is not totally rigid, but depending on your application, those tradeoffs could easily be worth it.
For more details, we spoke with first author Angus B. Clark via email.
IEEE Spectrum: Where did this idea come from?
Angus Clark: The idea of malleable robots came from the realization that the majority of serial robot arms have 6 or more degrees of freedom (DoF)—usually rotary joints—yet are typically performing tasks that only require 2 or 3 DoF. The idea of a robot arm that achieves flexibility and adaptation to tasks but maintains the simplicity of a low DoF system, along with the rapid development of variable stiffness continuum robots for medical applications, inspired us to develop the malleable robot concept.
What are some ways in which a malleable robot arm could provide unique advantages, and what are some potential applications that could leverage these advantages?
Malleable robots have the ability to complete multiple traditional tasks, such as pick and place or bin picking operations, without the added bulk of extra joints that are not directly used within each task, as the flexibility of the robot arm is provided by a malleable link instead. This results in an overall smaller form factor, including weight and footprint of the robot, as well as a lower power requirement and cost of the robot as fewer joints are needed, without sacrificing adaptability. This makes the robot ideal for scenarios where any of these factors are critical, such as in space robotics—where every kilogram saved is vital—or in rehabilitation robotics, where cost reduction may facilitate adoption, to name two examples. Moreover, the collaborative soft-robot-esque nature of malleable robots also tends towards collaborative robots in factories working safely alongside and with humans.
“The idea of malleable robots came from the realization that the majority of serial robot arms have 6 or more degrees of freedom (DoF), yet are typically performing tasks that only require 2 or 3 DoF”
—Angus B. Clark, Imperial College London
Compared to a conventional rigid link between joints, what are the disadvantages of using a malleable link?
Currently the maximum stiffness of a malleable link is considerably weaker than that of an equivalent solid steel rigid link, and this is one of the key areas we are focusing research on improving as motion precision and accuracy are impacted. We have created the largest existing variable stiffness link at roughly 800 mm length and 50 mm diameter, which suits malleable robots towards small and medium size workspaces. Our current results evaluating this accuracy are good, however achieving a uniform stiffness across the entire malleable link can be problematic due to the production of wrinkles under bending in the encapsulating membrane. As demonstrated by our SCARA topology results, this can produce slight structural variations resulting in reduced accuracy.
Does the robot have any way of knowing its own shape? Potentially, could this system reconfigure itself somehow?
Currently we compute the robot topology using motion tracking, with markers placed on the joints of the robot. Using distance geometry, we are then able to obtain the forward and inverse kinematics of the robot, of which we can use to control the end effector (the gripper) of the robot. Ideally, in the future we would love to develop a system that no longer requires the use of motion tracking cameras.
As for the robot reconfiguring itself, which we call an “intrinsic malleable link,” there are many methods that have been demonstrated for controlling a continuum structure, such as using positive pressure or via tendon wires, however the ability to in real-time determine the curvature of the link, not just the joint positions, is a significant hurdle to solve. However, we hope to see future development on malleable robots work towards solving this problem.
What are you working on next?
For us, refining the kinematics of the robot to enable a robust and complete system for allowing a user to collaboratively reshape the robot, while still achieving the accuracy expected from robotic systems, is our current main goal. Malleable robots are a brand new field we have introduced, and as such provide many opportunities for development and optimization. Over the coming years, we hope to see other researchers work alongside us to solve these problems.
“Design and Workspace Characterization of Malleable Robots,” by Angus B. Clark and Nicolas Rojas from Imperial College London, was presented at ICRA 2020.
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#437763 Peer Review of Scholarly Research Gets ...
In the world of academics, peer review is considered the only credible validation of scholarly work. Although the process has its detractors, evaluation of academic research by a cohort of contemporaries has endured for over 350 years, with “relatively minor changes.” However, peer review may be set to undergo its biggest revolution ever—the integration of artificial intelligence.
Open-access publisher Frontiers has debuted an AI tool called the Artificial Intelligence Review Assistant (AIRA), which purports to eliminate much of the grunt work associated with peer review. Since the beginning of June 2020, every one of the 11,000-plus submissions Frontiers received has been run through AIRA, which is integrated into its collaborative peer-review platform. This also makes it accessible to external users, accounting for some 100,000 editors, authors, and reviewers. Altogether, this helps “maximize the efficiency of the publishing process and make peer-review more objective,” says Kamila Markram, founder and CEO of Frontiers.
AIRA’s interactive online platform, which is a first of its kind in the industry, has been in development for three years.. It performs three broad functions, explains Daniel Petrariu, director of project management: assessing the quality of the manuscript, assessing quality of peer review, and recommending editors and reviewers. At the initial validation stage, the AI can make up to 20 recommendations and flag potential issues, including language quality, plagiarism, integrity of images, conflicts of interest, and so on. “This happens almost instantly and with [high] accuracy, far beyond the rate at which a human could be expected to complete a similar task,” Markram says.
“We have used a wide variety of machine-learning models for a diverse set of applications, including computer vision, natural language processing, and recommender systems,” says Markram. This includes simple bag-of-words models, as well as more sophisticated deep-learning ones. AIRA also leverages a large knowledge base of publications and authors.
Markram notes that, to address issues of possible AI bias, “We…[build] our own datasets and [design] our own algorithms. We make sure no statistical biases appear in the sampling of training and testing data. For example, when building a model to assess language quality, scientific fields are equally represented so the model isn’t biased toward any specific topic.” Machine- and deep-learning approaches, along with feedback from domain experts, including errors, are captured and used as additional training data. “By regularly re-training, we make sure our models improve in terms of accuracy and stay up-to-date.”
The AI’s job is to flag concerns; humans take the final decisions, says Petrariu. As an example, he cites image manipulation detection—something AI is super-efficient at but is nearly impossible for a human to perform with the same accuracy. “About 10 percent of our flagged images have some sort of problem,” he adds. “[In academic publishing] nobody has done this kind of comprehensive check [using AI] before,” says Petrariu. AIRA, he adds, facilitates Frontiers’ mission to make science open and knowledge accessible to all. Continue reading
#437751 Startup and Academics Find Path to ...
Engineers have been chasing a form of AI that could drastically lower the energy required to do typical AI things like recognize words and images. This analog form of machine learning does one of the key mathematical operations of neural networks using the physics of a circuit instead of digital logic. But one of the main things limiting this approach is that deep learning’s training algorithm, back propagation, has to be done by GPUs or other separate digital systems.
Now University of Montreal AI expert Yoshua Bengio, his student Benjamin Scellier, and colleagues at startup Rain Neuromorphics have come up with way for analog AIs to train themselves. That method, called equilibrium propagation, could lead to continuously learning, low-power analog systems of a far greater computational ability than most in the industry now consider possible, according to Rain CTO Jack Kendall.
Analog circuits could save power in neural networks in part because they can efficiently perform a key calculation, called multiply and accumulate. That calculation multiplies values from inputs according to various weights, and then it sums all those values up. Two fundamental laws of electrical engineering can basically do that, too. Ohm’s Law multiplies voltage and conductance to give current, and Kirchoff’s Current Law sums the currents entering a point. By storing a neural network’s weights in resistive memory devices, such as memristors, multiply-and-accumulate can happen completely in analog, potentially reducing power consumption by orders of magnitude.
The reason analog AI systems can’t train themselves today has a lot to do with the variability of their components. Just like real neurons, those in analog neural networks don’t all behave exactly alike. To do back propagation with analog components, you must build two separate circuit pathways. One going forward to come up with an answer (called inferencing), the other going backward to do the learning so that the answer becomes more accurate. But because of the variability of analog components, the pathways don't match up.
“You end up accumulating error as you go backwards through the network,” says Bengio. To compensate, a network would need lots of power-hungry analog-to-digital and digital-to-analog circuits, defeating the point of going analog.
Equilibrium propagation allows learning and inferencing to happen on the same network, partly by adjusting the behavior of the network as a whole. “What [equilibrium propagation] allows us to do is to say how we should modify each of these devices so that the overall circuit performs the right thing,” he says. “We turn the physical computation that is happening in the analog devices directly to our advantage.”
Right now, equilibrium propagation is only working in simulation. But Rain plans to have a hardware proof-of-principle in late 2021, according to CEO and cofounder Gordon Wilson. “We are really trying to fundamentally reimagine the hardware computational substrate for artificial intelligence, find the right clues from the brain, and use those to inform the design of this,” he says. The result could be what they call end-to-end analog AI systems that capable of running sophisticated robots or even playing a role in data centers. Both of those are currently considered beyond the capabilities of analog AI, which is now focused only on adding inferencing abilities to sensors and other low-power “edge” devices, while leaving the learning to GPUs. Continue reading