Tag Archives: testing
Cambridge, MA – Soft Robotics Inc, which has built a fundamentally new class of robotic grippers, announced the release of its expanded and upgraded Soft Robotics Development Kit; SRDK 2.0.
The Soft Robotics Development Kit 2.0 comes complete with:
Robot tool flange mounting plate
4, 5 and 6 position hub plates
Tool Center Point
Soft Robotics Control Unit G2
6 rail mounted, 4 accordion actuator modules
Custom pneumatic manifold
Mounting hardware and accessories
Where the SRDK 1.0 included 5 four accordion actuator modules and the opportunity to create a gripper containing two to five actuators, The SRDK 2.0 contains 6 four accordion actuator modules plus the addition of a six position hub allowing users the ability to configure six actuator test tools. This expands use of the Development Kit to larger product applications, such as: large bagged and pouched items, IV bags, bags of nuts, bread and other food items.
SRDK 2.0 also contains an upgraded Soft Robotics Control Unit (SRCU G2) – the proprietary system that controls all software and hardware with one turnkey pneumatic operation. The upgraded SRCU features new software with a cleaner, user friendly interface and an IP65 rating. Highly intuitive, the software is able to store up to eight grip profiles and allows for very precise adjustments to actuation and vacuum.
Also new with the release of SRDK 2.0, is the introduction of several accessory kits that will allow for an expanded number of configurations and product applications available for testing.
Accessory Kit 1 – For SRDK 1.0 users only – includes the six position hub and 4 accordion actuators now included in SRDK 2.0
Accessory Kit 2 – For SRDK 1.0 or 2.0 users – includes 2 accordion actuators
Accessory Kit 3 – For SRDK 1.0 or 2.0 users – includes 3 accordion actuators
The shorter 2 and 3 accordion actuators provide increased stability for high-speed applications, increased placement precision, higher grip force capabilities and are optimized for gripping small, shallow objects.
Designed to plug and play with any existing robot currently in the market, the Soft Robotics Development Kit 2.0 allows end-users and OEM Integrators the ability to customize, test and validate their ideal Soft Robotics solution, with their own equipment, in their own environment.
Once an ideal solution has been found, the Soft Robotics team will take those exact specifications and build a production-grade tool for implementation into the manufacturing line. And, it doesn’t end there. Created to be fully reusable, the process – configure, test, validate, build, production – can start over again as many times as needed.
See the new SRDK 2.0 on display for the first time at PACK EXPO Las Vegas, September 25 – 27, 2017 in Soft Robotics booth S-5925.
Learn more about the Soft Robotics Development Kit at www.softroboticsinc.com/srdk.
Photo Credit: Soft Robotics – www.softroboticsinc.com
About Soft Robotics
Soft Robotics designs and builds soft robotic gripping systems and automation solutions
that can grasp and manipulate items of varying size, shape and weight. Spun out of the
Whitesides Group at Harvard University, Soft Robotics is the only company to be
commercializing this groundbreaking and proprietary technology platform. Today, the
company is a global enterprise solving previously off-limits automation challenges for
customers in food & beverage, advanced manufacturing and ecommerce. Soft Robotics’
engineers are building an ecosystem of robots, control systems, data and machine
learning to enable the workplace of the future. For more information, please visit
The Kondracki Group, LLC
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Quantum computers could give the machine learning algorithms at the heart of modern artificial intelligence a dramatic speed up, but how far off are we? An international group of researchers has outlined the barriers that still need to be overcome.
This year has seen a surge of interest in quantum computing, driven in part by Google’s announcement that it will demonstrate “quantum supremacy” by the end of 2017. That means solving a problem beyond the capabilities of normal computers, which the company predicts will take 49 qubits—the quantum computing equivalent of bits.
As impressive as such a feat would be, the demonstration is likely to be on an esoteric problem that stacks the odds heavily in the quantum processor’s favor, and getting quantum computers to carry out practically useful calculations will take a lot more work.
But these devices hold great promise for solving problems in fields as diverse as cryptography or weather forecasting. One application people are particularly excited about is whether they could be used to supercharge the machine learning algorithms already transforming the modern world.
The potential is summarized in a recent review paper in the journal Nature written by a group of experts from the emerging field of quantum machine learning.
“Classical machine learning methods such as deep neural networks frequently have the feature that they can both recognize statistical patterns in data and produce data that possess the same statistical patterns: they recognize the patterns that they produce,” they write.
“This observation suggests the following hope. If small quantum information processors can produce statistical patterns that are computationally difficult for a classical computer to produce, then perhaps they can also recognize patterns that are equally difficult to recognize classically.”
Because of the way quantum computers work—taking advantage of strange quantum mechanical effects like entanglement and superposition—algorithms running on them should in principle be able to solve problems much faster than the best known classical algorithms, a phenomenon known as quantum speedup.
Designing these algorithms is tricky work, but the authors of the review note that there has been significant progress in recent years. They highlight multiple quantum algorithms exhibiting quantum speedup that could act as subroutines, or building blocks, for quantum machine learning programs.
We still don’t have the hardware to implement these algorithms, but according to the researchers the challenge is a technical one and clear paths to overcoming them exist. More challenging, they say, are four fundamental conceptual problems that could limit the applicability of quantum machine learning.
The first two are the input and output problems. Quantum computers, unsurprisingly, deal with quantum data, but the majority of the problems humans want to solve relate to the classical world. Translating significant amounts of classical data into the quantum systems can take so much time it can cancel out the benefits of the faster processing speeds, and the same is true of reading out the solution at the end.
The input problem could be mitigated to some extent by the development of quantum random access memory (qRAM)—the equivalent to RAM in a conventional computer used to provide the machine with quick access to its working memory. A qRAM can be configured to store classical data but allow the quantum computers to access all that information simultaneously as a superposition, which is required for a variety of quantum algorithms. But the authors note this is still a considerable engineering challenge and may not be sustainable for big data problems.
Closely related to the input/output problem is the costing problem. At present, the authors say very little is known about how many gates—or operations—a quantum machine learning algorithm will require to solve a given problem when operated on real-world devices. It’s expected that on highly complex problems they will offer considerable improvements over classical computers, but it’s not clear how big problems have to be before this becomes apparent.
Finally, whether or when these advantages kick in may be hard to prove, something the authors call the benchmarking problem. Claiming that a quantum algorithm can outperform any classical machine learning approach requires extensive testing against these other techniques that may not be feasible.
They suggest that this could be sidestepped by lowering the standards quantum machine learning algorithms are currently held to. This makes sense, as it doesn’t really matter whether an algorithm is intrinsically faster than all possible classical ones, as long as it’s faster than all the existing ones.
Another way of avoiding some of these problems is to apply these techniques directly to quantum data, the actual states generated by quantum systems and processes. The authors say this is probably the most promising near-term application for quantum machine learning and has the added benefit that any insights can be fed back into the design of better hardware.
“This would enable a virtuous cycle of innovation similar to that which occurred in classical computing, wherein each generation of processors is then leveraged to design the next-generation processors,” they conclude.
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Walking on two legs isn't as easy as it seems. For robots and their designers, it is an even bigger challenge! Researchers at EPFL's Biorobotics Laboratory are testing novel algorithms to improve humanoids' ability to walk and interact with humans. Continue reading
Each week we scour the web for great articles and fascinating advances across our core topics, from AI to biotech and the brain. But robots have a special place in our hearts. This week, we took a look back at 2017 so far and unearthed a few favorite robots for your reading and viewing pleasure.
Tarzan the Swinging Robot Could Be the Future of FarmingMariella Moon | Engadget“Tarzan will be able to swing over crops using its 3D-printed claws and parallel guy-wires stretched over fields. It will then take measurements and pictures of each plant with its built-in camera while suspended…While it may take some time to achieve that goal, the researchers plan to start testing the robot soon.”
Grasping Robots Compete to Rule Amazon’s Warehouses Tom Simonite | Wired“Robots able to help with so-called picking tasks would boost Amazon’s efficiency—and make it much less reliant on human workers. It’s why the company has invited a motley crew of mechanical arms, grippers, suction cups—and their human handlers—to Nagoya, Japan, this week to show off their manipulation skills.”
Robots Learn to Speak Body LanguageAlyssa Pagano | IEEE Spectrum“One notable feature of the OpenPose system is that it can track not only a person’s head, torso, and limbs but also individual fingers. To do that, the researchers used CMU’s Panoptic Studio, a dome lined with 500 cameras, where they captured body poses at a variety of angles and then used those images to build a data set.”
I Watched Two Robots Chat Together on Stage at a Tech EventJon Russell | TechCrunch“The robots in question are Sophia and Han, and they belong to Hanson Robotics, a Hong Kong-based company that is developing and deploying artificial intelligence in humanoids. The duo took to the stage at Rise in Hong Kong with Hanson Robotics’ Chief Scientist Ben Goertzel directing the banter. The conversation, which was partially scripted, wasn’t as slick as the human-to-human panels at the show, but it was certainly a sight to behold for the packed audience.”
How This Japanese Robotics Master Is Building Better, More Human AndroidsHarry McCracken | Fast Company“On the tech side, making a robot look and behave like a person involves everything from electronics to the silicone Ishiguro’s team uses to simulate skin. ‘We have a technology to precisely control pneumatic actuators,’ he says, noting, as an example of what they need to re-create, that ‘the human shoulder has four degrees of freedom.’”
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