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Giving soft robots feeling | MIT News

One of the hottest topics in robotics is the field of soft robots, which utilizes squishy and flexible materials rather than traditional rigid materials. But soft robots have been limited due to their lack of good sensing. A good robotic gripper needs to feel what it is touching (tactile sensing), and it needs to sense the positions of its fingers (proprioception). Such sensing has been missing from most soft robots.

In a new pair of papers, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) came up with new tools to let robots better perceive what they’re interacting with: the ability to see and classify items, and a softer, delicate touch. 

“We wish to enable seeing the world by feeling the world. Soft robot hands have sensorized skins that allow them to pick up a range of objects, from delicate, such as potato chips, to heavy, such as milk bottles,” says CSAIL Director Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and the deputy dean of research for the MIT Stephen A. Schwarzman College of Computing. 

One paper builds off last year’s research from MIT and Harvard University, where a team developed a soft and strong robotic gripper in the form of a cone-shaped origami structure. It collapses in on objects much like a Venus’ flytrap, to pick up items that are as much as 100 times its weight. 

To get that newfound versatility and adaptability even closer to that of a human hand, a new team came up with a sensible addition: tactile sensors, made from latex “bladders” (balloons) connected to pressure transducers. The new sensors let the gripper not only pick up objects as delicate as potato chips, but it also classifies them — letting the robot better understand what it’s picking up, while also exhibiting that light touch. 

When classifying objects, the sensors correctly identified 10 objects with over 90 percent accuracy, even when an object slipped out of grip.

“Unlike many other soft tactile sensors, ours can be rapidly fabricated, retrofitted into grippers, and show sensitivity and reliability,” says MIT postdoc Josie Hughes, the lead author on a new paper about the sensors. “We hope they provide a new method of soft sensing that can be applied to a wide range of different applications in manufacturing settings, like packing and lifting.” 

In a second paper, a group of researchers created a soft robotic finger called “GelFlex” that uses embedded cameras and deep learning to enable high-resolution tactile sensing and “proprioception” (awareness of positions and movements of the body). 

The gripper, which looks much like a two-finger cup gripper you might see at a soda station, uses a tendon-driven mechanism to actuate the fingers. When tested on metal objects of various shapes, the system had over 96 percent recognition accuracy. 

“Our soft finger can provide high accuracy on proprioception and accurately predict grasped objects, and also withstand considerable impact without harming the interacted environment and itself,” says Yu She, lead author on a new paper on GelFlex. “By constraining soft fingers with a flexible exoskeleton, and performing high-resolution sensing with embedded cameras, we open up a large range of capabilities for soft manipulators.” 

Magic ball senses 

The magic ball gripper is made from a soft origami structure, encased by a soft balloon. When a vacuum is applied to the balloon, the origami structure closes around the object, and the gripper deforms to its structure. 

While this motion lets the gripper grasp a much wider range of objects than ever before, such as soup cans, hammers, wine glasses, drones, and even a single broccoli floret, the greater intricacies of delicacy and understanding were still out of reach — until they added the sensors.  

When the sensors experience force or strain, the internal pressure changes, and the team can measure this change in pressure to identify when it will feel that again. 

In addition to the latex sensor, the team also developed an algorithm which uses feedback to let the gripper possess a human-like duality of being both strong and precise — and 80 percent of the tested objects were successfully grasped without damage. 

The team tested the gripper-sensors on a variety of household items, ranging from heavy bottles to small, delicate objects, including cans, apples, a toothbrush, a water bottle, and a bag of cookies. 

Going forward, the team hopes to make the methodology scalable, using computational design and reconstruction methods to improve the resolution and coverage using this new sensor technology. Eventually, they imagine using the new sensors to create a fluidic sensing skin that shows scalability and sensitivity. 

Hughes co-wrote the new paper with Rus, which they will present virtually at the 2020 International Conference on Robotics and Automation. 

GelFlex

In the second paper, a CSAIL team looked at giving a soft robotic gripper more nuanced, human-like senses. Soft fingers allow a wide range of deformations, but to be used in a controlled way there must be rich tactile and proprioceptive sensing. The team used embedded cameras with wide-angle “fisheye” lenses that capture the finger’s deformations in great detail.

To create GelFlex, the team used silicone material to fabricate the soft and transparent finger, and put one camera near the fingertip and the other in the middle of the finger. Then, they painted reflective ink on the front and side surface of the finger, and added LED lights on the back. This allows the internal fish-eye camera to observe the status of the front and side surface of the finger. 

The team trained neural networks to extract key information from the internal cameras for feedback. One neural net was trained to predict the bending angle of GelFlex, and the other was trained to estimate the shape and size of the objects being grabbed. The gripper could then pick up a variety of items such as a Rubik’s cube, a DVD case, or a block of aluminum. 

During testing, the average positional error while gripping was less than 0.77 millimeter, which is better than that of a human finger. In a second set of tests, the gripper was challenged with grasping and recognizing cylinders and boxes of various sizes. Out of 80 trials, only three were classified incorrectly. 

In the future, the team hopes to improve the proprioception and tactile sensing algorithms, and utilize vision-based sensors to estimate more complex finger configurations, such as twisting or lateral bending, which are challenging for common sensors, but should be attainable with embedded cameras.

Yu She co-wrote the GelFlex paper with MIT graduate student Sandra Q. Liu, Peiyu Yu of Tsinghua University, and MIT Professor Edward Adelson. They will present the paper virtually at the 2020 International Conference on Robotics and Automation.

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Engineers put tens of thousands of artificial brain synapses on a single chip | MIT News

MIT engineers have designed a “brain-on-a-chip,” smaller than a piece of confetti, that is made from tens of thousands of artificial brain synapses known as memristors — silicon-based components that mimic the information-transmitting synapses in the human brain.

The researchers borrowed from principles of metallurgy to fabricate each memristor from alloys of silver and copper, along with silicon. When they ran the chip through several visual tasks, the chip was able to “remember” stored images and reproduce them many times over, in versions that were crisper and cleaner compared with existing memristor designs made with unalloyed elements.

Their results, published today in the journal Nature Nanotechnology, demonstrate a promising new memristor design for neuromorphic devices — electronics that are based on a new type of circuit that processes information in a way that mimics the brain’s neural architecture. Such brain-inspired circuits could be built into small, portable devices, and would carry out complex computational tasks that only today’s supercomputers can handle.

“So far, artificial synapse networks exist as software. We’re trying to build real neural network hardware for portable artificial intelligence systems,” says Jeehwan Kim, associate professor of mechanical engineering at MIT. “Imagine connecting a neuromorphic device to a camera on your car, and having it recognize lights and objects and make a decision immediately, without having to connect to the internet. We hope to use energy-efficient memristors to do those tasks on-site, in real-time.”

Wandering ions

Memristors, or memory transistors, are an essential element in neuromorphic computing. In a neuromorphic device, a memristor would serve as the transistor in a circuit, though its workings would more closely resemble a brain synapse — the junction between two neurons. The synapse receives signals from one neuron, in the form of ions, and sends a corresponding signal to the next neuron.

A transistor in a conventional circuit transmits information by switching between one of only two values, 0 and 1, and doing so only when the signal it receives, in the form of an electric current, is of a particular strength. In contrast, a memristor would work along a gradient, much like a synapse in the brain. The signal it produces would vary depending on the strength of the signal that it receives. This would enable a single memristor to have many values, and therefore carry out a far wider range of operations than binary transistors.

Like a brain synapse, a memristor would also be able to “remember” the value associated with a given current strength, and produce the exact same signal the next time it receives a similar current. This could ensure that the answer to a complex equation, or the visual classification of an object, is reliable — a feat that normally involves multiple transistors and capacitors.

Ultimately, scientists envision that memristors would require far less chip real estate than conventional transistors, enabling powerful, portable computing devices that do not rely on supercomputers, or even connections to the Internet.

Existing memristor designs, however, are limited in their performance. A single memristor is made of a positive and negative electrode, separated by a “switching medium,” or space between the electrodes. When a voltage is applied to one electrode, ions from that electrode flow through the medium, forming a “conduction channel” to the other electrode. The received ions make up the electrical signal that the memristor transmits through the circuit. The size of the ion channel (and the signal that the memristor ultimately produces) should be proportional to the strength of the stimulating voltage.

Kim says that existing memristor designs work pretty well in cases where voltage stimulates a large conduction channel, or a heavy flow of ions from one electrode to the other. But these designs are less reliable when memristors need to generate subtler signals, via thinner conduction channels.

The thinner a conduction channel, and the lighter the flow of ions from one electrode to the other, the harder it is for individual ions to stay together. Instead, they tend to wander from the group, disbanding within the medium. As a result, it’s difficult for the receiving electrode to reliably capture the same number of ions, and therefore transmit the same signal, when stimulated with a certain low range of current.

Borrowing from metallurgy

Kim and his colleagues found a way around this limitation by borrowing a technique from metallurgy, the science of melding metals into alloys and studying their combined properties.

“Traditionally, metallurgists try to add different atoms into a bulk matrix to strengthen materials, and we thought, why not tweak the atomic interactions in our memristor, and add some alloying element to control the movement of ions in our medium,” Kim says.

Engineers typically use silver as the material for a memristor’s positive electrode. Kim’s team looked through the literature to find an element that they could combine with silver to effectively hold silver ions together, while allowing them to flow quickly through to the other electrode.

The team landed on copper as the ideal alloying element, as it is able to bind both with silver, and with silicon.

“It acts as a sort of bridge, and stabilizes the silver-silicon interface,” Kim says.

To make memristors using their new alloy, the group first fabricated a negative electrode out of silicon, then made a positive electrode by depositing a slight amount of copper, followed by a layer of silver. They sandwiched the two electrodes around an amorphous silicon medium. In this way, they patterned a millimeter-square silicon chip with tens of thousands of memristors.

As a first test of the chip, they recreated a gray-scale image of the Captain America shield. They equated each pixel in the image to a corresponding memristor in the chip. They then modulated the conductance of each memristor that was relative in strength to the color in the corresponding pixel.

The chip produced the same crisp image of the shield, and was able to “remember” the image and reproduce it many times, compared with chips made of other materials.

The team also ran the chip through an image processing task, programming the memristors to alter an image, in this case of MIT’s Killian Court, in several specific ways, including sharpening and blurring the original image. Again, their design produced the reprogrammed images more reliably than existing memristor designs.

“We’re using artificial synapses to do real inference tests,” Kim says. “We would like to develop this technology further to have larger-scale arrays to do image recognition tasks. And some day, you might be able to carry around artificial brains to do these kinds of tasks, without connecting to supercomputers, the internet, or the cloud.”

This research was funded, in part, by the MIT Research Support Committee funds, the MIT-IBM Watson AI Lab, Samsung Global Research Laboratory, and the National Science Foundation.

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Learning the ropes and throwing lifelines | MIT News

In March, as her friends and neighbors were scrambling to pack up and leave campus due to the Covid-19 pandemic, Geeticka Chauhan found her world upended in yet another way. Just weeks earlier, she had been elected council president of MIT’s largest graduate residence, Sidney-Pacific. Suddenly the fourth-year PhD student was plunged into rounds of emergency meetings with MIT administrators.

From her apartment in Sidney-Pacific, where she has stayed put due to travel restrictions in her home country of India, Chauhan is still learning the ropes of her new position. With others, she has been busy preparing to meet the future challenge of safely redensifying the living space of more than 1,000 people: how to regulate high-density common areas, handle noise complaints as people spend more time in their rooms, and care for the mental and physical well-being of a community that can only congregate virtually. “It’s just such a crazy time,” she says.

She’s prepared for the challenge. During her time at MIT, while pursuing her research using artificial intelligence to understand human language, Chauhan has worked to strengthen the bonds of her community in numerous ways, often drawing on her experience as an international student to do so.

Adventures in brunching

When Chauhan first came to MIT in 2017, she quickly fell in love with Sidney-Pacific’s thriving and freewheeling “helper culture.” “These are all researchers, but they’re maybe making brownies, doing crazy experiments that they would do in lab, except in the kitchen,” she says. “That was my first introduction to the MIT spirit.”

Next thing she knew, she was teaching Budokon yoga, mashing chickpeas into guacamole, and immersing herself in the complex operations of a monthly brunch attended by hundreds of graduate students, many of whom came to MIT from outside the U.S. In addition to the genuine thrill of cracking 300 eggs in 30 minutes, working on the brunches kept her grounded in a place thousands of miles from her home in New Delhi. “It gave me a sense of community and made me feel like I have a family here,” she says.

Chauhan has found additional ways to address the particular difficulties that international students face. As a member of the Presidential Advisory Council this year, she gathered international student testimonies on visa difficulties and presented them to MIT’s president and the director of the International Students Office. And when a friend from mainland China had to self-quarantine on Valentine’s Day, Chauhan knew she had to act. As brunch chair, she organized food delivery, complete with chocolates and notes, for Sidney-Pacific residents who couldn’t make it to the monthly event. “Initially when you come back to the U.S. from your home country, you really miss your family,” she says. “I thought self-quarantining students should feel their MIT community cares for them.”

Culture shock

Growing up in New Delhi, math was initially one of her weaknesses, Chauhan says, and she was scared and confused by her early introduction to coding. Her mother and grandmother, with stern kindness and chocolates, encouraged her to face these fears. “My mom used to teach me that with hard work, you can make your biggest weakness your biggest strength,” she explains. She soon set her sights on a future in computer science.

However, as Chauhan found her life increasingly dominated by the high-pressure culture of preparing for college, she began to long for a feeling of wholeness, and for the person she left behind on the way. “I used to have a lot of artistic interests but didn’t get to explore them,” she says. She quit her weekend engineering classes, enrolled in a black and white photography class, and after learning about the extracurricular options at American universities, landed a full scholarship to attend Florida International University.

It was a culture shock. She didn’t know many Indian students in Miami and felt herself struggling to reconcile the individualistic mindset around her with the community and family-centered life at home. She says the people she met got her through, including Mark Finlayson, a professor studying the science of narrative from the viewpoint of natural language processing. Under Finlayson’s guidance she developed a fascination with the way AI techniques could be used to better understand the patterns and structures in human narratives. She learned that studying AI wasn’t just a way of imitating human thinking, but rather an approach for deepening our understanding of ourselves as reflected by our language. “It was due to Mark’s mentorship that I got involved in research” and applied to MIT, she says.

The holistic researcher

Chauan now works in the Clinical Decision Making Group led by Peter Szolovits at the Computer Science and Artificial Intelligence Laboratory, where she is focusing on the ways natural language processing can address health care problems. For her master’s project, she worked on the problem of relation extraction and built a tool to digest clinical literature that would, for example, help pharamacologists easily assess negative drug interactions. Now, she’s finishing up a project integrating visual analysis of chest radiographs and textual analysis of radiology reports for quantifying pulmonary edema, to help clinicians manage the fluid status of their patients who have suffered acute heart failure.

“In routine clinical practice, patient care is interweaved with a lot of bureaucratic work,” she says. “The goal of my lab is to assist with clinical decision making and give clinicians the full freedom and time to devote to patient care.”

It’s an exciting moment for Chauhan, who recently submitted a paper she co-first authored with another grad student, and is starting to think about her next project: interpretability, or how to elucidate a decision-making model’s “thought process” by highlighting the data from which it draws its conclusions. She continues to find the intersection of computer vision and natural language processing an exciting area of research. But there have been challenges along the way.

After the initial flurry of excitement her first year, personal and faculty expectations of students’ independence and publishing success grew, and she began to experience uncertainty and imposter syndrome. “I didn’t know what I was capable of,” she says. “That initial period of convincing yourself that you belong is difficult. I am fortunate to have a supportive advisor that understands that.”

Finally, one of her first-year projects showed promise, and she came up with a master’s thesis plan in a month and submitted the project that semester. To get through, she says, she drew on her “survival skills”: allowing herself to be a full person beyond her work as a researcher so that one setback didn’t become a sense of complete failure. For Chauhan, that meant working as a teaching assistant, drawing henna designs, singing, enjoying yoga, and staying involved in student government. “I used to try to separate that part of myself with my work side,” she says. “I needed to give myself some space to learn and grow, rather than compare myself to others.”

Citing a study showing that women are more likely to drop out of STEM disciplines when they receive a B grade in a challenging course, Chauhan says she wishes she could tell her younger self not to compare herself with an ideal version of herself. Dismantling imposter syndrome requires an understanding that qualification and success can come from a broad range of experiences, she says: It’s about “seeing people for who they are holistically, rather than what is seen on the resume.”

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