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#435687 Humanoid Robots Teach Coping Skills to ...
Photo: Rob Felt
IEEE Senior Member Ayanna Howard with one of the interactive androids that help children with autism improve their social and emotional engagement.
THE INSTITUTEChildren with autism spectrum disorder can have a difficult time expressing their emotions and can be highly sensitive to sound, sight, and touch. That sometimes restricts their participation in everyday activities, leaving them socially isolated. Occupational therapists can help them cope better, but the time they’re able to spend is limited and the sessions tend to be expensive.
Roboticist Ayanna Howard, an IEEE senior member, has been using interactive androids to guide children with autism on ways to socially and emotionally engage with others—as a supplement to therapy. Howard is chair of the School of Interactive Computing and director of the Human-Automation Systems Lab at Georgia Tech. She helped found Zyrobotics, a Georgia Tech VentureLab startup that is working on AI and robotics technologies to engage children with special needs. Last year Forbes named Howard, Zyrobotics’ chief technology officer, one of the Top 50 U.S. Women in Tech.
In a recent study, Howard and other researchers explored how robots might help children navigate sensory experiences. The experiment involved 18 participants between the ages of 4 and 12; five had autism, and the rest were meeting typical developmental milestones. Two humanoid robots were programmed to express boredom, excitement, nervousness, and 17 other emotional states. As children explored stations set up for hearing, seeing, smelling, tasting, and touching, the robots modeled what the socially acceptable responses should be.
“If a child’s expression is one of happiness or joy, the robot will have a corresponding response of encouragement,” Howard says. “If there are aspects of frustration or sadness, the robot will provide input to try again.” The study suggested that many children with autism exhibit stronger levels of engagement when the robots interact with them at such sensory stations.
It is one of many robotics projects Howard has tackled. She has designed robots for researching glaciers, and she is working on assistive robots for the home, as well as an exoskeleton that can help children who have motor disabilities.
Howard spoke about her work during the Ethics in AI: Impacts of (Anti?) Social Robotics panel session held in May at the IEEE Vision, Innovation, and Challenges Summit in San Diego. You can watch the session on IEEE.tv.
The next IEEE Vision, Innovation, and Challenges Summit and Honors Ceremony will be held on 15 May 2020 at the JW Marriott Parq Vancouver hotel, in Vancouver.
In this interview with The Institute, Howard talks about how she got involved with assistive technologies, the need for a more diverse workforce, and ways IEEE has benefited her career.
FOCUS ON ACCESSIBILITY
Howard was inspired to work on technology that can improve accessibility in 2008 while teaching high school students at a summer camp devoted to science, technology, engineering, and math.
“A young lady with a visual impairment attended camp. The robot programming tools being used at the camp weren’t accessible to her,” Howard says. “As an engineer, I want to fix problems when I see them, so we ended up designing tools to enable access to programming tools that could be used in STEM education.
“That was my starting motivation, and this theme of accessibility has expanded to become a main focus of my research. One of the things about this world of accessibility is that when you start interacting with kids and parents, you discover another world out there of assistive technologies and how robotics can be used for good in education as well as therapy.”
DIVERSITY OF THOUGHT
The Institute asked Howard why it’s important to have a more diverse STEM workforce and what could be done to increase the number of women and others from underrepresented groups.
“The makeup of the current engineering workforce isn’t necessarily representative of the world, which is composed of different races, cultures, ages, disabilities, and socio-economic backgrounds,” Howard says. “We’re creating products used by people around the globe, so we have to ensure they’re being designed for a diverse population. As IEEE members, we also need to engage with people who aren’t engineers, and we don’t do that enough.”
Educational institutions are doing a better job of increasing diversity in areas such as gender, she says, adding that more work is needed because the enrollment numbers still aren’t representative of the population and the gains don’t necessarily carry through after graduation.
“There has been an increase in the number of underrepresented minorities and females going into engineering and computer science,” she says, “but data has shown that their numbers are not sustained in the workforce.”
ROLE MODEL
Because there are more underrepresented groups on today’s college campuses that can form a community, the lack of engineering role models—although a concern on campuses—is more extreme for preuniversity students, Howard says.
“Depending on where you go to school, you may not know what an engineer does or even consider engineering as an option,” she says, “so there’s still a big disconnect there.”
Howard has been involved for many years in math- and science-mentoring programs for at-risk high school girls. She tells them to find what they’re passionate about and combine it with math and science to create something. She also advises them not to let anyone tell them that they can’t.
Howard’s father is an engineer. She says he never encouraged or discouraged her to become one, but when she broke something, he would show her how to fix it and talk her through the process. Along the way, he taught her a logical way of thinking she says all engineers have.
“When I would try to explain something, he would quiz me and tell me to ‘think more logically,’” she says.
Howard earned a bachelor’s degree in engineering from Brown University, in Providence, R.I., then she received both a master’s and doctorate degree in electrical engineering from the University of Southern California. Before joining the faculty of Georgia Tech in 2005, she worked at NASA’s Jet Propulsion Laboratory at the California Institute of Technology for more than a decade as a senior robotics researcher and deputy manager in the Office of the Chief Scientist.
ACTIVE VOLUNTEER
Howard’s father was also an IEEE member, but that’s not why she joined the organization. She says she signed up when she was a student because, “that was something that you just did. Plus, my student membership fee was subsidized.”
She kept the membership as a grad student because of the discounted rates members receive on conferences.
Those conferences have had an impact on her career. “They allow you to understand what the state of the art is,” she says. “Back then you received a printed conference proceeding and reading through it was brutal, but by attending it in person, you got a 15-minute snippet about the research.”
Howard is an active volunteer with the IEEE Robotics and Automation and the IEEE Systems, Man, and Cybernetics societies, holding many positions and serving on several committees. She is also featured in the IEEE Impact Creators campaign. These members were selected because they inspire others to innovate for a better tomorrow.
“I value IEEE for its community,” she says. “One of the nice things about IEEE is that it’s international.” Continue reading
Posted in Human Robots
#435640 Video Friday: This Wearable Robotic Tail ...
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, Pa., USA
CLAWAR 2019 – August 26-28, 2019 – Kuala Lumpur, Malaysia
IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.
Lakshmi Nair from Georgia Tech describes some fascinating research towards robots that can create their own tools, as presented at ICRA this year:
Using a novel capability to reason about shape, function, and attachment of unrelated parts, researchers have for the first time successfully trained an intelligent agent to create basic tools by combining objects.
The breakthrough comes from Georgia Tech’s Robot Autonomy and Interactive Learning (RAIL) research lab and is a significant step toward enabling intelligent agents to devise more advanced tools that could prove useful in hazardous – and potentially life-threatening – environments.
[ Lakshmi Nair ]
Victor Barasuol, from the Dynamic Legged Systems Lab at IIT, wrote in to share some new research on their HyQ quadruped that enables sensorless shin collision detection. This helps the robot navigate unstructured environments, and also mitigates all those painful shin strikes, because ouch.
This will be presented later this month at the International Conference on Climbing and Walking Robots (CLAWAR) in Kuala Lumpur, Malaysia.
[ IIT ]
Thanks Victor!
You used to have a tail, you know—as an embryo, about a month in to your development. All mammals used to have tails, and now we just have useless tailbones, which don’t help us with balancing even a little bit. BRING BACK THE TAIL!
The tail, created by Junichi Nabeshima, Kouta Minamizawa, and MHD Yamen Saraiji from Keio University’s Graduate School of Media Design, was presented at SIGGRAPH 2019 Emerging Technologies.
[ Paper ] via [ Gizmodo ]
The noises in this video are fantastic.
[ ESA ]
Apparently the industrial revolution wasn’t a thorough enough beatdown of human knitting, because the robots are at it again.
[ MIT CSAIL ]
Skydio’s drones just keep getting more and more impressive. Now if only they’d make one that I can afford…
[ Skydio ]
The only thing more fun than watching robots is watching people react to robots.
[ SEER ]
There aren’t any robots in this video, but it’s robotics-related research, and very soothing to watch.
[ Stanford ]
#autonomousicecreamtricycle
In case it wasn’t clear, which it wasn’t, this is a Roboy project. And if you didn’t understand that first video, you definitely won’t understand this second one:
Whatever that t-shirt is at the end (Roboy in sunglasses puking rainbows…?) I need one.
[ Roboy ]
By adding electronics and computation technology to a simple cane that has been around since ancient times, a team of researchers at Columbia Engineering have transformed it into a 21st century robotic device that can provide light-touch assistance in walking to the aged and others with impaired mobility.
The light-touch robotic cane, called CANINE, acts as a cane-like mobile assistant. The device improves the individual’s proprioception, or self-awareness in space, during walking, which in turn improves stability and balance.
[ ROAR Lab ]
During the second field experiment for DARPA’s OFFensive Swarm-Enabled Tactics (OFFSET) program, which took place at Fort Benning, Georgia, teams of autonomous air and ground robots tested tactics on a mission to isolate an urban objective. Similar to the way a firefighting crew establishes a boundary around a burning building, they first identified locations of interest and then created a perimeter around the focal point.
[ DARPA ]
I think there’s a bit of new footage here of Ghost Robotics’ Vision 60 quadruped walking around without sensors on unstructured terrain.
[ Ghost Robotics ]
If you’re as tired of passenger drone hype as I am, there’s absolutely no need to watch this video of NEC’s latest hover test.
[ AP ]
As researchers teach robots to perform more and more complex tasks, the need for realistic simulation environments is growing. Existing techniques for closing the reality gap by approximating real-world physics often require extensive real world data and/or thousands of simulation samples. This paper presents TuneNet, a new machine learning-based method to directly tune the parameters of one model to match another using an iterative residual tuning technique. TuneNet estimates the parameter difference between two models using a single observation from the target and minimal simulation, allowing rapid, accurate and sample-efficient parameter estimation.
The system can be trained via supervised learning over an auto-generated simulated dataset. We show that TuneNet can perform system identification, even when the true parameter values lie well outside the distribution seen during training, and demonstrate that simulators tuned with TuneNet outperform existing techniques for predicting rigid body motion. Finally, we show that our method can estimate real-world parameter values, allowing a robot to perform sim-to-real task transfer on a dynamic manipulation task unseen during training. We are also making a baseline implementation of our code available online.
[ Paper ]
Here’s an update on what GITAI has been up to with their telepresence astronaut-replacement robot.
[ GITAI ]
Curiosity captured this 360-degree panorama of a location on Mars called “Teal Ridge” on June 18, 2019. This location is part of a larger region the rover has been exploring called the “clay-bearing unit” on the side of Mount Sharp, which is inside Gale Crater. The scene is presented with a color adjustment that approximates white balancing to resemble how the rocks and sand would appear under daytime lighting conditions on Earth.
[ MSL ]
Some updates (in English) on ROS from ROSCon France. The first is a keynote from Brian Gerkey:
And this second video is from Omri Ben-Bassat, about how to keep your Anki Vector alive using ROS:
All of the ROSCon FR talks are available on Vimeo.
[ ROSCon FR ] Continue reading
Posted in Human Robots
#435474 Watch China’s New Hybrid AI Chip Power ...
When I lived in Beijing back in the 90s, a man walking his bike was nothing to look at. But today, I did a serious double-take at a video of a bike walking his man.
No kidding.
The bike itself looks overloaded but otherwise completely normal. Underneath its simplicity, however, is a hybrid computer chip that combines brain-inspired circuits with machine learning processes into a computing behemoth. Thanks to its smart chip, the bike self-balances as it gingerly rolls down a paved track before smoothly gaining speed into a jogging pace while navigating dexterously around obstacles. It can even respond to simple voice commands such as “speed up,” “left,” or “straight.”
Far from a circus trick, the bike is a real-world demo of the AI community’s latest attempt at fashioning specialized hardware to keep up with the challenges of machine learning algorithms. The Tianjic (天机*) chip isn’t just your standard neuromorphic chip. Rather, it has the architecture of a brain-like chip, but can also run deep learning algorithms—a match made in heaven that basically mashes together neuro-inspired hardware and software.
The study shows that China is readily nipping at the heels of Google, Facebook, NVIDIA, and other tech behemoths investing in developing new AI chip designs—hell, with billions in government investment it may have already had a head start. A sweeping AI plan from 2017 looks to catch up with the US on AI technology and application by 2020. By 2030, China’s aiming to be the global leader—and a champion for building general AI that matches humans in intellectual competence.
The country’s ambition is reflected in the team’s parting words.
“Our study is expected to stimulate AGI [artificial general intelligence] development by paving the way to more generalized hardware platforms,” said the authors, led by Dr. Luping Shi at Tsinghua University.
A Hardware Conundrum
Shi’s autonomous bike isn’t the first robotic two-wheeler. Back in 2015, the famed research nonprofit SRI International in Menlo Park, California teamed up with Yamaha to engineer MOTOBOT, a humanoid robot capable of driving a motorcycle. Powered by state-of-the-art robotic hardware and machine learning, MOTOBOT eventually raced MotoGPTM world champion Valentino Rossi in a nail-biting match-off.
However, the technological core of MOTOBOT and Shi’s bike vastly differ, and that difference reflects two pathways towards more powerful AI. One, exemplified by MOTOBOT, is software—developing brain-like algorithms with increasingly efficient architecture, efficacy, and speed. That sounds great, but deep neural nets demand so many computational resources that general-purpose chips can’t keep up.
As Shi told China Science Daily: “CPUs and other chips are driven by miniaturization technologies based on physics. Transistors might shrink to nanoscale-level in 10, 20 years. But what then?” As more transistors are squeezed onto these chips, efficient cooling becomes a limiting factor in computational speed. Tax them too much, and they melt.
For AI processes to continue, we need better hardware. An increasingly popular idea is to build neuromorphic chips, which resemble the brain from the ground up. IBM’s TrueNorth, for example, contains a massively parallel architecture nothing like the traditional Von Neumann structure of classic CPUs and GPUs. Similar to biological brains, TrueNorth’s memory is stored within “synapses” between physical “neurons” etched onto the chip, which dramatically cuts down on energy consumption.
But even these chips are limited. Because computation is tethered to hardware architecture, most chips resemble just one specific type of brain-inspired network called spiking neural networks (SNNs). Without doubt, neuromorphic chips are highly efficient setups with dynamics similar to biological networks. They also don’t play nicely with deep learning and other software-based AI.
Brain-AI Hybrid Core
Shi’s new Tianjic chip brought the two incompatibilities together onto a single piece of brainy hardware.
First was to bridge the deep learning and SNN divide. The two have very different computation philosophies and memory organizations, the team said. The biggest difference, however, is that artificial neural networks transform multidimensional data—image pixels, for example—into a single, continuous, multi-bit 0 and 1 stream. In contrast, neurons in SNNs activate using something called “binary spikes” that code for specific activation events in time.
Confused? Yeah, it’s hard to wrap my head around it too. That’s because SNNs act very similarly to our neural networks and nothing like computers. A particular neuron needs to generate an electrical signal (a “spike”) large enough to transfer down to the next one; little blips in signals don’t count. The way they transmit data also heavily depends on how they’re connected, or the network topology. The takeaway: SNNs work pretty differently than deep learning.
Shi’s team first recreated this firing quirk in the language of computers—0s and 1s—so that the coding mechanism would become compatible with deep learning algorithms. They then carefully aligned the step-by-step building blocks of the two models, which allowed them to tease out similarities into a common ground to further build on. “On the basis of this unified abstraction, we built a cross-paradigm neuron scheme,” they said.
In general, the design allowed both computational approaches to share the synapses, where neurons connect and store data, and the dendrites, the outgoing branches of the neurons. In contrast, the neuron body, where signals integrate, was left reconfigurable for each type of computation, as were the input branches. Each building block was combined into a single unified functional core (FCore), which acts like a deep learning/SNN converter depending on its specific setup. Translation: the chip can do both types of previously incompatible computation.
The Chip
Using nanoscale fabrication, the team arranged 156 FCores, containing roughly 40,000 neurons and 10 million synapses, onto a chip less than a fifth of an inch in length and width. Initial tests showcased the chip’s versatility, in that it can run both SNNs and deep learning algorithms such as the popular convolutional neural network (CNNs) often used in machine vision.
Compared to IBM TrueNorth, the density of Tianjic’s cores increased by 20 percent, speeding up performance ten times and increasing bandwidth at least 100-fold, the team said. When pitted against GPUs, the current hardware darling of machine learning, the chip increased processing throughput up to 100 times, while using just a sliver (1/10,000) of energy.
Although these stats are great, real-life performance is even better as a demo. Here’s where the authors gave their Tianjic brain a body. The team combined one chip with multiple specialized networks to process vision, balance, voice commands, and decision-making in real time. Object detection and target tracking, for example, relied on a deep neural net CNN, whereas voice commands and balance data were recognized using an SNN. The inputs were then integrated inside a neural state machine, which churned out decisions to downstream output modules—for example, controlling the handle bar to turn left.
Thanks to the chip’s brain-like architecture and bilingual ability, Tianjic “allowed all of the neural network models to operate in parallel and realized seamless communication across the models,” the team said. The result is an autonomous bike that rolls after its human, balances across speed bumps, avoids crashing into roadblocks, and answers to voice commands.
General AI?
“It’s a wonderful demonstration and quite impressive,” said the editorial team at Nature, which published the study on its cover last week.
However, they cautioned, when comparing Tianjic with state-of-the-art chips designed for a single problem toe-to-toe on that particular problem, Tianjic falls behind. But building these jack-of-all-trades hybrid chips is definitely worth the effort. Compared to today’s limited AI, what people really want is artificial general intelligence, which will require new architectures that aren’t designed to solve one particular problem.
Until people start to explore, innovate, and play around with different designs, it’s not clear how we can further progress in the pursuit of general AI. A self-driving bike might not be much to look at, but its hybrid brain is a pretty neat place to start.
*The name, in Chinese, means “heavenly machine,” “unknowable mystery of nature,” or “confidentiality.” Go figure.
Image Credit: Alexander Ryabintsev / Shutterstock.com Continue reading
Posted in Human Robots