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Wednesday, August 23rd, 2017
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| 10:00a |
Custom robots in a matter of minutes Even as robots become increasingly common, they remain incredibly difficult to make. From designing and modeling to fabricating and testing, the process is slow and costly: Even one small change can mean days or weeks of rethinking and revising important hardware.
But what if there were a way to let non-experts craft different robotic designs — in one sitting?
Researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are getting closer to doing exactly that. In a new paper, they present a system called “Interactive Robogami” that lets you design a robot in minutes, and then 3-D print and assemble it in as little as four hours.
One of the key features of the system is that it allows designers to determine both the robot’s movement (“gait”) and shape (“geometry”), a capability that’s often separated in design systems.
“Designing robots usually requires expertise that only mechanical engineers and roboticists have,” says PhD student and co-lead author Adriana Schulz. “What’s exciting here is that we’ve created a tool that allows a casual user to design their own robot by giving them this expert knowledge.”
The paper, which is being published in the new issue of the International Journal of Robotics Research, was co-led by PhD graduate Cynthia Sung alongside MIT professors Wojciech Matusik and Daniela Rus.
The other co-authors include PhD student Andrew Spielberg, former master’s student Wei Zhao, former undergraduate Robin Cheng, and Columbia University professor Eitan Grinspun. (Sung is now an assistant professor at the University of Pennsylvania.)
How it works
3-D printing has transformed the way that people can turn ideas into real objects, allowing users to move away from more traditional manufacturing. Despite these developments, current design tools still have space and motion limitations, and there’s a steep learning curve to understanding the various nuances.
Interactive Robogami aims to be much more intuitive. It uses simulations and interactive feedback with algorithms for design composition, allowing users to focus on high-level conceptual design. Users can choose from a library of over 50 different bodies, wheels, legs, and “peripherals,” as well as a selection of different steps (“gaits”).
Importantly, the system is able to guarantee that a design is actually possible, analyzing factors such as speed and stability to make suggestions and ensure that, for example, the user doesn’t create a robot so top-heavy that it can’t move without tipping over.
Once designed, the robot is then fabricated. The team’s origami-inspired “3-D print and fold” technique involves printing the design as flat faces connected at joints, and then folding the design into the final shape, combining the most effective parts of 2-D and 3-D printing.
“3-D printing lets you print complex, rigid structures, while 2-D fabrication gives you lightweight but strong structures that can be produced quickly,” Sung says. “By 3-D printing 2-D patterns, we can leverage these advantages to develop strong, complex designs with lightweight materials.”
Results
To test the system, the team used eight subjects who were given 20 minutes of training and asked to perform two tasks.
One task involved creating a mobile, stable car design in just 10 minutes. In a second task, users were given a robot design and asked to create a trajectory to navigate the robot through an obstacle course in the least amount of travel time.
The team fabricated a total of six robots, each of which took 10 to 15 minutes to design, three to seven hours to print and 30 to 90 minutes to assemble. The team found that their 3-D print-and-fold method reduced printing time by 73 percent and the amount of material used by 70 percent. The robots also demonstrated a wide range of movement, like using single legs to walk, using different step sequences, and using legs and wheels simultaneously.
“You can quickly design a robot that you can print out, and that will help you do these tasks very quickly, easily, and cheaply,” says Sung. “It’s lowering the barrier to have everyone design and create their own robots.”
Rus hopes people will be able to incorporate robots to help with everyday tasks, and that similar systems with rapid printing technologies will enable large-scale customization and production of robots.
"These tools enable new approaches to teaching computational thinking and creating,” says Rus. “Students can not only learn by coding and making their own robots, but by bringing to life conceptual ideas about what their robots can actually do.”
While the current version focuses on designs that can walk, the team hopes that in the future, the robots can take flight. Another goal is to have the user be able to go into the system and define the behavior of the robot in terms of tasks it can perform.
"This tool enables rapid exploration of dynamic robots at an early stage in the design process," says Moritz Bächer, a research scientist and head of the computational design and manufacturing group at Disney Research. “The expert defines the building blocks, with constraints and composition rules, and paves the way for non-experts to make complex robotic systems. This system will likely inspire follow-up work targeting the computational design of even more intricate robots.”
This research was supported by the National Science Foundation's Expeditions in Computing program. | | 3:30p |
Monitoring network traffic more efficiently In today’s data networks, traffic analysis — determining which links are getting congested and why — is usually done by computers at the network’s edge, which try to infer the state of the network from the times at which different data packets reach their destinations.
If the routers inside the network could instead report on their own circumstances, network analysis would be much more precise and efficient, enabling network operators to more rapidly address problems. To that end, router manufacturers have begun equipping their routers with counters that can report on the number of data packets a router has processed in a given time interval.
But raw number counts are only so useful, and giving routers a special-purpose monitoring circuit for every new measurement an operator might want to make isn’t practical. The alternative is for routers to ship data packets to outside servers for more complex analysis, but that technique doesn’t scale well. A data center with 100,000 servers, for instance, might need another 40,000 to 50,000 servers just to keep up with the flood of router data.
Researchers at MIT, Cisco Systems, and Barefoot Networks have come up with a new approach to network monitoring that provides great flexibility in data collection while keeping both the circuit complexity of the router and the number of external analytic servers low. They describe the work in a paper they’re presenting this week at the annual conference of the Association for Computing Machinery’s Special Interest Group on Data Communication.
Dubbed Marple, the system consists of a programming language that enables network operators to specify a wide range of network-monitoring tasks and a small set of simple circuit elements that can execute any task specified in the language. Simulations using actual data center traffic statistics suggest that, in the data center setting, Marple should require only one traffic analysis server for every 40 or 50 application servers.
Future-proofing
“There’s this big movement toward making routers programmable and making the hardware itself programmable,” says Mohammad Alizadeh, the TIBCO Career Development Assistant Professor of Electrical Engineering and Computer Science at MIT and a senior author on the paper. “So we were really motivated to think about what this would mean for network-performance monitoring and measurement. What would I want to be able to program into the router to make the task of the network operator easier?
“We realized that it’s going to be very difficult to try to figure this out by picking out some measurement primitives or algorithms that we know of and saying, here’s a module that will allow you to do this, here’s a module that will allow you to do that. It would be difficult to get something that’s future-proof and general using that approach.”
Instead, Alizadeh and his collaborators co-designed the Marple language and the circuitry required to implement Marple queries, with one eye on the expressive flexibility of the language and another on the complexity of the circuits required to realize that flexibility. The team included first author Srinivas Narayana, a postdoc at MIT’s Computer Science and Artificial Intelligence Laboratory; Anirudh Sivaraman, Vikram Nathan, and Prateesh Goyal, all MIT graduate students in electrical engineering and computer science; Venkat Arun, an undergraduate at the Indian Institute of Technology Guwahati who visited MIT for a summer; Vimalkumar Jeyakumar of Cisco Tetration Analytics; and Changhoon Kim of Barefoot Networks.
The idea behind Marple is to do as much analysis on the router itself as possible without causing network delays, and then to send the external server summary statistics rather than raw packet data, incurring huge savings in both bandwidth and processing time.
Marple is designed to individually monitor the transmissions of every computer sending data through a router, a number that can easily top 1 million. The problem is that a typical router has enough memory to store statistics on only 64,000 connections or so.
One-way cache
Marple solves this problem through a variation on the common computer science technique of caching, in which frequently used data is stored close to a processing unit for efficient access. Each router has a cache in which it maintains statistics on the data packets it’s seen from some fixed number of senders — say, 64,000. If its cache is full, and it receives a packet from yet another sender — the 64,001st — it simply kicks out the data associated with one of the previous 64,000 senders, shipping it off to a support server for storage. If it later receives another packet from the sender it booted, it starts a new cache entry for that sender.
This approach works only if newly booted data can be merged with the data already stored on the server. In the case of packet counting, this is simple enough. If the server records that a given router saw 1,000 packets from sender A, and if the router has seen another 100 packets from sender A since it last emptied A’s cache, then at the next update the server simply adds the new 100 packets to the 1,000 it’s already recorded.
But the merge process is not so straightforward if the statistic of interest is a weighted average of the number of packets processed per minute or the rate at which packets have been dropped by the network. The researchers’ paper, however, includes a theoretical analysis showing that merging is always possible for statistics that are “linear in state.”
“Linear” means that any update to the statistic involves multiplying its current value by one number and then adding another number to that product. The “in state” part means that the multiplier and the addend can be the results of mathematical operations performed on some number of previous packet measurements.
“We found that for operations where it wasn’t immediately clear how they’d be written in this form, there was always a way to rewrite them into this form,” Narayana says. “So it turns out to be a fairly useful class of operations, practically.”
"While much work has been done on low-level programmable primitives for measuring performance, these features are impotent without an easier network programming environment so that operators can ask network-level queries without writing low-level queries on multiple routers,” says George Varghese, Chancellor's Professor of Computer Science at the University of California at Los Angeles. “This paper represents an important step toward a programming-language approach to networks, starting with a network programming abstraction. This is in stark contrast to the state of the art today, which is individual router programming, which is fault prone and gives little visibility into the network as a whole. Further, the network programming language is intuitive, using familiar functional-language primitives, reducing the learning curve for operators."
The new work was supported by the National Science Foundation, the U.S. Defense Advanced Projects Agency, and Cisco Systems. | | 11:59p |
Experiments confirm theory of “superballistic” electron flow When many people try to squeeze through a passageway at the same time, it creates a bottleneck that slows everyone down. It turns out the reverse is true for electrons, which can move through small openings more quickly when travelling in large groups than when flying solo.
The theory of so-called superballistic flow predicts that electrons can pass more easily through constrictions by interacting with one another, and thereby “cooperating,” than they can individually. The theory was proposed in a paper earlier this year by a team led by MIT professor of physics Leonid Levitov.
Now, in a paper published this week in the journal Nature Physics, a team at the University of Manchester in the U.K., working alongside Levitov and MIT undergraduate Haoyu Guo, have confirmed the theory in an experiment employing devices built from an atomically thin layer of graphene.
The idea behind superballistic flow is that interactions among electrons make them move in a highly coordinated manner, mimicking the behavior of particles in highly viscous fluids.
When electrons traveling individually pass through a constricted opening, they will bounce off the walls at either side, losing their momentum as well as some of their energy.
But when the electrons travel in dense groups, they are much more likely to bounce off each other than the walls. Such electron-electron collisions are known as “lossless,” since the total energy and the net momentum of the two particles are conserved. The momentum of individual electrons can change rapidly in the process, however the overall momentum conservation ensures that the losses are very low.
As a result, together the electrons are able to travel more quickly, and pass through the constriction more easily, than they would alone.
“Viscous flows of electrons have been anticipated in theory but never observed, partly because the materials were not good enough at the time, and partly because there were no good proposals of what to look for,” Levitov says.
To make viscous flow easier to identify, Levitov’s theoretical paper suggested forcing electrons to travel through a constriction, generating an electric current. This is a similar idea to the way in which 19th century researchers studied viscosity by passing fluids through a narrow channel.
“If you run current through a constriction, and the conditions are right and the flow is viscous … the resistance of that flow will be anomalously low, namely lower than that expected for free particle flow,” Levitov says.
This drop in resistance can be measured, revealing the presence of viscous flow.
Using the experimental set-up described theoretically in Levitov’s previous paper, the Manchester researchers, led by professor of physics and Nobel laureate Andre Geim, carefully etched a series of constrictions, or pinch points, within pieces of graphene encapsulated between boron-nitride crystals.
“The team etched the graphene sheets into a shape where they formed several constrictions, arranged in sequence, and they then applied a current such that it flowed through all of these constrictions one by one,” Levitov says.
The researchers then measured the drop in electric potential over each constriction independently, allowing them to detect the flow rate through each pinch point in the device.
They found that the conductance of the electrons exceeded the maximum conductance possible for free electrons, known as Landauer’s ballistic limit.
They also found that the conductance of the electrons increased with a rise in temperature.
In this way the researchers were able to verify Levitov and Guo’s original predictions within just a few days. Levitov says this is probably the fastest experimental confirmation of one of his predictions in his entire career, with the longest taking around 20 years to prove.
To confirm their findings, the researchers then repeated the experiment with a range of different graphene devices, and obtained the same results.
The work points toward the possibility of using interactions among electrons to design low-power electronics, Levitov says.
But more fundamentally, he says, it opens up new territory in our understanding of charge flow physics, in which electrons behave in a collective manner.
Electron-electron interactions have been responsible for a huge variety of novel and exciting physics, but the effects of these interactions typically become stronger as the temperature is reduced, says Amir Yacoby, a professor of physics at Harvard University, who was not involved in the research.
“The hydrodynamic electron flow regime is yet another incredibly rich manifestation of electron-electron interactions, and this time it grows with increasing temperature,” Yacoby says.
This suggests that some of these effects might become more accessible to observation than ever before.
“The particular phenomena described in the theory and experiment are a beautiful example of a new regime of conductance that has not been explored before,” he says.
Levitov and his team are now investigating the implications of these findings. In particular they plan to study heat transport within the new fluid mechanics regime.
“It looks like heat transport in this new regime is also very surprising, and more interesting than we initially thought,” he says. “This fluid mechanics regime could possibly be used to control heat flow in electronic systems in new ways.” |
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