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#435683 How High Fives Help Us Get in Touch With ...

Posted on September 16, 2019 by Android

The human sense of touch is so naturally ingrained in our everyday lives that we often don’t notice its presence. Even so, touch is a crucial sensing ability that helps people to understand the world and connect with others. As the market for robots grows, and as robots become more ingrained into our environments, people will expect robots to participate in a wide variety of social touch interactions. At Oregon State University’s Collaborative Robotics and Intelligent Systems (CoRIS) Institute, I research how to equip everyday robots with better social-physical interaction skills—from playful high-fives to challenging physical therapy routines.

Some commercial robots already possess certain physical interaction skills. For example, the videoconferencing feature of mobile telepresence robots can keep far-away family members connected with one another. These robots can also roam distant spaces and bump into people, chairs, and other remote objects. And my Roomba occasionally tickles my toes before turning to vacuum a different area of the room. As a human being, I naturally interpret this (and other Roomba behaviors) as social, even if they were not intended as such. At the same time, for both of these systems, social perceptions of the robots’ physical interaction behaviors are not well understood, and these social touch-like interactions cannot be controlled in nuanced ways.

Before joining CoRIS early this year, I was a postdoc at the University of Southern California’s Interaction Lab, and prior to that, I completed my doctoral work at the GRASP Laboratory’s Haptics Group at the University of Pennsylvania. My dissertation focused on improving the general understanding of how robot control and planning strategies influence perceptions of social touch interactions. As part of that research, I conducted a study of human-robot hand-to-hand contact, focusing on an interaction somewhere between a high five and a hand-clapping game. I decided to study this particular interaction because people often high five, and they will likely expect robots in everyday spaces to high five as well!

I conducted a study of human-robot hand-to-hand contact, focusing on an interaction somewhere between a high five and a hand-clapping game. I decided to study this particular interaction because people often high five, and they will likely expect robots to high five as well!

The implications of motion and planning on the social touch experience in these interactions is also crucial—think about a disappointingly wimpy (or triumphantly amazing) high five that you’ve experienced in the past. This great or terrible high-fiving experience could be fleeting, but it could also influence who you interact with, who you’re friends with, and even how you perceive the character or personalities of those around you. This type of perception, judgement, and response could extend to personal robots, too!

An investigation like this requires a mixture of more traditional robotics research (e.g., understanding how to move and control a robot arm, developing models of the desired robot motion) along with techniques from design and psychology (e.g., performing interviews with research participants, using best practices from experimental methods in perception). Enabling robots with social touch abilities also comes with many challenges, and even skilled humans can have trouble anticipating what another person is about to do. Think about trying to make satisfying hand contact during a high five—you might know the classic adage “watch the elbow,” but if you’re like me, even this may not always work.

I conducted a research study involving eight different types of human-robot hand contact, with different combinations of the following: interactions with a facially reactive or non-reactive robot, a physically reactive or non-reactive planning strategy, and a lower or higher robot arm stiffness. My robotic system could become facially reactive by changing its facial expression in response to hand contact, or physically reactive by updating its plan of where to move next after sensing hand contact. The stiffness of the robot could be adjusted by changing a variable that controlled how quickly the robot’s motors tried to pull its arm to the desired position. I knew from previous research that fine differences in touch interactions can have a big impact on perceived robot character. For example, if a robot grips an object too tightly or for too long while handing an object to a person, it might be perceived as greedy, possessive, or perhaps even Sméagol-like. A robot that lets go too soon might appear careless or sloppy.

In the example cases of robot grip, it’s clear that understanding people’s perceptions of robot characteristics and personality can help roboticists choose the right robot design based on the proposed operating environment of the robot. I likewise wanted to learn how the facial expressions, physical reactions, and stiffness of a hand-clapping robot would influence human perceptions of robot pleasantness, energeticness, dominance, and safety. Understanding this relationship can help roboticists to equip robots with personalities appropriate for the task at hand. For example, a robot assisting people in a grocery store may need to be designed with a high level of pleasantness and only moderate energy, while a maximally effective robot for comedy roast battles may need high degrees of energy and dominance above all else.

After many a late night at the GRASP Lab clapping hands with a big red robot, I was ready to conduct the study. Twenty participants visited the lab to clap hands with our Baxter Research Robot and help me begin to understand how characteristics of this humanoid robot’s social touch influenced its pleasantness, energeticness, dominance, and apparent safety. Baxter interacted with participants using a custom 3D-printed hand that was inlaid with silicone inserts.

The study showed that a facially reactive robot seemed more pleasant and energetic. A physically reactive robot seemed less pleasant, energetic, and dominant for this particular study design and interaction. I thought contact with a stiffer robot would seem harder (and therefore more dominant and less safe), but counter to my expectations, a stiffer-armed robot seemed safer and less dominant to participants. This may be because the stiffer robot was more precise in following its pre-programmed trajectory, therefore seeming more predictable and less free-spirited.

Safety ratings of the robot were generally high, and several participants commented positively on the robot’s facial expressions. Some participants attributed inventive (and non-existent) intelligences to the robot—I used neither computer vision nor the Baxter robot’s cameras in this study, but more than one participant complimented me on how well the robot tracked their hand position. While interacting with the robot, participants displayed happy facial expressions more than any other analyzed type of expression.

Photo: Naomi Fitter

Participants were asked to clap hands with Baxter and describe how they perceived the robot in terms of its pleasantness, energeticness, dominance, and apparent safety.

Circling back to the idea of how people might interpret even rudimentary and practical robot behaviors as social, these results show that this type of social perception isn’t just true for my lovable (but sometimes dopey) Roomba, but also for collaborative industrial robots, and generally, any robot capable of physical human-robot interaction. In designing the motion of Baxter, the adjustment of a single number in the equation that controls joint stiffness can flip the robot from seeming safe and docile to brash and commanding. These implications are sometimes predictable, but often unexpected.

The results of this particular study give us a partial guide to manipulating the emotional experience of robot users by adjusting aspects of robot control and planning, but future work is needed to fully understand the design space of social touch. Will materials play a major role? How about personalized machine learning? Do results generalize over all robot arms, or even a specialized subset like collaborative industrial robot arms? I’m planning to continue answering these questions, and when I finally solve human-robot social touch, I’ll high five all my robots to celebrate.

Naomi Fitter is an assistant professor in the Collaborative Robotics and Intelligent Systems (CoRIS) Institute at Oregon State University, where her Social Haptics, Assistive Robotics, and Embodiment (SHARE) research group aims to equip robots with the ability to engage and empower people in interactions from playful high-fives to challenging physical therapy routines. She completed her doctoral work in the GRASP Laboratory’s Haptics Group and was a postdoctoral scholar in the University of Southern California’s Interaction Lab from 2017 to 2018. Naomi’s not-so-secret pastime is performing stand-up and improv comedy. Continue reading →

Posted in Human Robots

#435681 Video Friday: This NASA Robot Uses ...

Posted on September 16, 2019 by Android

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!):

ICRES 2019 – July 29-30, 2019 – London, U.K.
DARPA SubT Tunnel Circuit – August 15-22, 2019 – Pittsburgh, Pa., USA
IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Let us know if you have suggestions for next week, and enjoy today’s videos.

Robots can land on the Moon and drive on Mars, but what about the places they can’t reach? Designed by engineers as NASA’s Jet Propulsion Laboratory in Pasadena, California, a four-limbed robot named LEMUR (Limbed Excursion Mechanical Utility Robot) can scale rock walls, gripping with hundreds of tiny fishhooks in each of its 16 fingers and using artificial intelligence to find its way around obstacles. In its last field test in Death Valley, California, in early 2019, LEMUR chose a route up a cliff, scanning the rock for ancient fossils from the sea that once filled the area.

The LEMUR project has since concluded, but it helped lead to a new generation of walking, climbing and crawling robots. In future missions to Mars or icy moons, robots with AI and climbing technology derived from LEMUR could discover similar signs of life. Those robots are being developed now, honing technology that may one day be part of future missions to distant worlds.

[ NASA ]

This video demonstrates the autonomous footstep planning developed by IHMC. Robots in this video are the Atlas humanoid robot (DRC version) and the NASA Valkyrie. The operator specifies a goal location in the world, which is modeled as planar regions using the robot’s perception sensors. The planner then automatically computes the necessary steps to reach the goal using a Weighted A* algorithm. The algorithm does not reject footholds that have a certain amount of support, but instead modifies them after the plan is found to try and increase that support area.

Currently, narrow terrain has a success rate of about 50%, rough terrain is about 90%, whereas flat ground is near 100%. We plan on increasing planner speed and the ability to plan through mazes and to unseen goals by including a body-path planner as the first step. Control, Perception, and Planning algorithms by IHMC Robotics.

[ IHMC ]

I’ve never really been able to get into watching people play poker, but throw an AI from CMU and Facebook into a game of no-limit Texas hold’em with five humans, and I’m there.

[ Facebook ]

In this video, Cassie Blue is navigating autonomously. Right now, her world is very small, the Wavefield at the University of Michigan, where she is told to turn left at intersections. You’re right, that is not a lot of independence, but it’s a first step away from a human and an RC controller!

Using a RealSense RGBD Camera, an IMU, and our version of an InEKF with contact factors, Cassie Blue is building a 3D semantic map in real time that identifies sidewalks, grass, poles, bicycles, and buildings. From the semantic map, occupancy and cost maps are built with the sidewalk identified as walk-able area and everything else considered as an obstacle. A planner then sets a goal to stay approximately 50 cm to the right of the sidewalk’s left edge and plans a path around obstacles and corners using D*. The path is translated into way-points that are achieved via Cassie Blue’s gait controller.

[ University of Michigan ]

Thanks Jesse!

Dave from HEBI Robotics wrote in to share some new actuators that are designed to get all kinds of dirty: “The R-Series takes HEBI’s X-Series to the next level, providing a sealed robotics solution for rugged, industrial applications and laying the groundwork for industrial users to address challenges that are not well met by traditional robotics. To prove it, we shot some video right in the Allegheny River here in Pittsburgh. Not a bad way to spend an afternoon :-)”

The R-Series Actuator is a full-featured robotic component as opposed to a simple servo motor. The output rotates continuously, requires no calibration or homing on boot-up, and contains a thru-bore for easy daisy-chaining of wiring. Modular in nature, R-Series Actuators can be used in everything from wheeled robots to collaborative robotic arms. They are sealed to IP67 and designed with a lightweight form factor for challenging field applications, and they’re packed with sensors that enable simultaneous control of position, velocity, and torque.

[ HEBI Robotics ]

Thanks Dave!

If your robot hands out karate chops on purpose, that’s great. If it hands out karate chops accidentally, maybe you should fix that.

COVR is short for “being safe around collaborative and versatile robots in shared spaces”. Our mission is to significantly reduce the complexity in safety certifying cobots. Increasing safety for collaborative robots enables new innovative applications, thus increasing production and job creation for companies utilizing the technology. Whether you’re an established company seeking to deploy cobots or an innovative startup with a prototype of a cobot related product, COVR will help you analyze, test and validate the safety for that application.

[ COVR ]

Thanks Anna!

EPFL startup Flybotix has developed a novel drone with just two propellers and an advanced stabilization system that allow it to fly for twice as long as conventional models. That fact, together with its small size, makes it perfect for inspecting hard-to-reach parts of industrial facilities such as ducts.

[ Flybotix ]

SpaceBok is a quadruped robot designed and built by a Swiss student team from ETH Zurich and ZHAW Zurich, currently being tested using Automation and Robotics Laboratories (ARL) facilities at our technical centre in the Netherlands. The robot is being used to investigate the potential of ‘dynamic walking’ and jumping to get around in low gravity environments.

SpaceBok could potentially go up to 2 m high in lunar gravity, although such a height poses new challenges. Once it comes off the ground the legged robot needs to stabilise itself to come down again safely – like a mini-spacecraft. So, like a spacecraft. SpaceBok uses a reaction wheel to control its orientation.

[ ESA ]

A new video from GITAI showing progress on their immersive telepresence robot for space.

[ GITAI ]

Tech United’s HERO robot (a Toyota HSR) competed in the RoboCup@Home competition, and it had a couple of garbage-related hiccups.

[ Tech United ]

Even small drones are getting better at autonomous obstacle avoidance in cluttered environments at useful speeds, as this work from the HKUST Aerial Robotics Group shows.

[ HKUST ]

DelFly Nimbles now come in swarms.

[ DelFly Nimble ]

This is a very short video, but it’s a fairly impressive look at a Baxter robot collaboratively helping someone put a shirt on, a useful task for folks with disabilities.

[ Shibata Lab ]

ANYmal can inspect the concrete in sewers for deterioration by sliding its feet along the ground.

[ ETH Zurich ]

HUG is a haptic user interface for teleoperating advanced robotic systems as the humanoid robot Justin or the assistive robotic system EDAN. With its lightweight robot arms, HUG can measure human movements and simultaneously display forces from the distant environment. In addition to such teleoperation applications, HUG serves as a research platform for virtual assembly simulations, rehabilitation, and training.

[ DLR ]

This video about “image understanding” from CMU in 1979 (!) is amazing, and even though it’s long, you won’t regret watching until 3:30. Or maybe you will.

[ ARGOS (pdf) ]

Will Burrard-Lucas’ BeetleCam turned 10 this month, and in this video, he recounts the history of his little robotic camera.

[ BeetleCam ]

In this week’s episode of Robots in Depth, Per speaks with Gabriel Skantze from Furhat Robotics.

Gabriel Skantze is co-founder and Chief Scientist at Furhat Robotics and Professor in speech technology at KTH with a specialization in conversational systems. He has a background in research into how humans use spoken communication to interact.

In this interview, Gabriel talks about how the social robot revolution makes it necessary to communicate with humans in a human ways through speech and facial expressions. This is necessary as we expand the number of people that interact with robots as well as the types of interaction. Gabriel gives us more insight into the many challenges of implementing spoken communication for co-bots, where robots and humans work closely together. They need to communicate about the world, the objects in it and how to handle them. We also get to hear how having an embodied system using the Furhat robot head helps the interaction between humans and the system.

[ Robots in Depth ] Continue reading →

Posted in Human Robots

#435676 Intel’s Neuromorphic System Hits 8 ...

Posted on September 16, 2019 by Android

At the DARPA Electronics Resurgence Initiative Summit today in Detroit, Intel plans to unveil an 8-million-neuron neuromorphic system comprising 64 Loihi research chips—codenamed Pohoiki Beach. Loihi chips are built with an architecture that more closely matches the way the brain works than do chips designed to do deep learning or other forms of AI. For the set of problems that such “spiking neural networks” are particularly good at, Loihi is about 1,000 times as fast as a CPU and 10,000 times as energy efficient. The new 64-Loihi system represents the equivalent of 8-million neurons, but that’s just a step to a 768-chip, 100-million-neuron system that the company plans for the end of 2019.

Intel and its research partners are just beginning to test what massive neural systems like Pohoiki Beach can do, but so far the evidence points to even greater performance and efficiency, says Mike Davies, director of neuromorphic research at Intel.

“We’re quickly accumulating results and data that there are definite benefits… mostly in the domain of efficiency. Virtually every one that we benchmark…we find significant gains in this architecture,” he says.

Going from a single-Loihi to 64 of them is more of a software issue than a hardware one. “We designed scalability into the Loihi chip from the beginning,” says Davies. “The chip has a hierarchical routing interface…which allows us to scale to up to 16,000 chips. So 64 is just the next step.”

Photo: Tim Herman/Intel Corporation

One of Intel’s Nahuku boards, each of which contains 8 to 32 Intel Loihi neuromorphic chips, shown here interfaced to an Intel Arria 10 FPGA development kit. Intel’s latest neuromorphic system, Pohoiki Beach, is made up of multiple Nahuku boards and contains 64 Loihi chips.

Finding algorithms that run well on an 8-million-neuron system and optimizing those algorithms in software is a considerable effort, he says. Still, the payoff could be huge. Neural networks that are more brain-like, such as Loihi, could be immune to some of the artificial intelligence’s—for lack of a better word—dumbness.

For example, today’s neural networks suffer from something called catastrophic forgetting. If you tried to teach a trained neural network to recognize something new—a new road sign, say—by simply exposing the network to the new input, it would disrupt the network so badly that it would become terrible at recognizing anything. To avoid this, you have to completely retrain the network from the ground up. (DARPA’s Lifelong Learning, or L2M, program is dedicated to solving this problem.)

(Here’s my favorite analogy: Say you coached a basketball team, and you raised the net by 30 centimeters while nobody was looking. The players would miss a bunch at first, but they’d figure things out quickly. If those players were like today’s neural networks, you’d have to pull them off the court and teach them the entire game over again—dribbling, passing, everything.)

Loihi can run networks that might be immune to catastrophic forgetting, meaning it learns a bit more like a human. In fact, there’s evidence through a research collaboration with Thomas Cleland’s group at Cornell University, that Loihi can achieve what’s called one-shot learning. That is, learning a new feature after being exposed to it only once. The Cornell group showed this by abstracting a model of the olfactory system so that it would run on Loihi. When exposed to a new virtual scent, the system not only didn't catastrophically forget everything else it had smelled, it learned to recognize the new scent just from the single exposure.

Loihi might also be able to run feature-extraction algorithms that are immune to the kinds of adversarial attacks that befuddle today’s image recognition systems. Traditional neural networks don’t really understand the features they’re extracting from an image in the way our brains do. “They can be fooled with simplistic attacks like changing individual pixels or adding a screen of noise that wouldn’t fool a human in any way,” Davies explains. But the sparse-coding algorithms Loihi can run work more like the human visual system and so wouldn’t fall for such shenanigans. (Disturbingly, humans are not completely immune to such attacks.)

Photo: Tim Herman/Intel Corporation

A close-up shot of Loihi, Intel’s neuromorphic research chip. Intel’s latest neuromorphic system, Pohoiki Beach, will be comprised of 64 of these Loihi chips.

Researchers have also been using Loihi to improve real-time control for robotic systems. For example, last week at the Telluride Neuromorphic Cognition Engineering Workshop—an event Davies called “summer camp for neuromorphics nerds”—researchers were hard at work using a Loihi-based system to control a foosball table. “It strikes people as crazy,” he says. “But it’s a nice illustration of neuromorphic technology. It’s fast, requires quick response, quick planning, and anticipation. These are what neuromorphic chips are good at.” Continue reading →

Posted in Human Robots

#435674 MIT Future of Work Report: We ...

Posted on September 16, 2019 by Android

Robots aren’t going to take everyone’s jobs, but technology has already reshaped the world of work in ways that are creating clear winners and losers. And it will continue to do so without intervention, says the first report of MIT’s Task Force on the Work of the Future.

The supergroup of MIT academics was set up by MIT President Rafael Reif in early 2018 to investigate how emerging technologies will impact employment and devise strategies to steer developments in a positive direction. And the headline finding from their first publication is that it’s not the quantity of jobs we should be worried about, but the quality.

Widespread press reports of a looming “employment apocalypse” brought on by AI and automation are probably wide of the mark, according to the authors. Shrinking workforces as developed countries age and outstanding limitations in what machines can do mean we’re unlikely to have a shortage of jobs.

But while unemployment is historically low, recent decades have seen a polarization of the workforce as the number of both high- and low-skilled jobs have grown at the expense of the middle-skilled ones, driving growing income inequality and depriving the non-college-educated of viable careers.

This is at least partly attributable to the growth of digital technology and automation, the report notes, which are rendering obsolete many middle-skilled jobs based around routine work like assembly lines and administrative support.

That leaves workers to either pursue high-skilled jobs that require deep knowledge and creativity, or settle for low-paid jobs that rely on skills—like manual dexterity or interpersonal communication—that are still beyond machines, but generic to most humans and therefore not valued by employers. And the growth of emerging technology like AI and robotics is only likely to exacerbate the problem.

This isn’t the first report to note this trend. The World Bank’s 2016 World Development Report noted how technology is causing a “hollowing out” of labor markets. But the MIT report goes further in saying that the cause isn’t simply technology, but the institutions and policies we’ve built around it.

The motivation for introducing new technology is broadly assumed to be to increase productivity, but the authors note a rarely-acknowledged fact: “Not all innovations that raise productivity displace workers, and not all innovations that displace workers substantially raise productivity.”

Examples of the former include computer-aided design software that makes engineers and architects more productive, while examples of the latter include self-service checkouts and automated customer support that replace human workers, often at the expense of a worse customer experience.

While the report notes that companies have increasingly adopted the language of technology augmenting labor, in reality this has only really benefited high-skilled workers. For lower-skilled jobs the motivation is primarily labor cost savings, which highlights the other major force shaping technology’s impact on employment: shareholder capitalism.

The authors note that up until the 1980s, increasing productivity resulted in wage growth across the economic spectrum, but since then average wage growth has failed to keep pace and gains have dramatically skewed towards the top earners.

The report shies away from directly linking this trend to the birth of Reaganomics (something others have been happy to do), but it notes that American veneration of the shareholder as the primary stakeholder in a business and tax policies that incentivize investment in capital rather than labor have exacerbated the negative impacts technology can have on employment.

That means the current focus on re-skilling workers to thrive in the new economy is a necessary, but not sufficient, solution to the disruptive impact technology is having on work, the authors say.

Alongside significant investment in education, fiscal policies need to be re-balanced away from subsidizing investment in physical capital and towards boosting investment in human capital, the authors write, and workers need to have a greater say in corporate decision-making.

The authors point to other developed economies where productivity growth, income growth, and equality haven’t become so disconnected thanks to investments in worker skills, social safety nets, and incentives to invest in human capital. Whether such a radical reshaping of US economic policy is achievable in today’s political climate remains to be seen, but the authors conclude with a call to arms.

“The failure of the US labor market to deliver broadly shared prosperity despite rising productivity is not an inevitable byproduct of current technologies or free markets,” they write. “We can and should do better.”

Image Credit: Simon Abrams / Unsplash/a> Continue reading →

Posted in Human Robots

#435669 Watch World Champion Soccer Robots Take ...

Posted on September 15, 2019 by Android

RoboCup 2019 took place earlier this month down in Sydney, Australia. While there are many different events including RoboCup@Home, RoboCup Rescue, and a bunch of different soccer leagues, one of the most compelling events is middle-size league (MSL), where mobile robots each about the size of a fire hydrant play soccer using a regular size FIFA soccer ball. The robots are fully autonomous, making their own decisions in real time about when to dribble, pass, and shoot.

The long-term goal of RoboCup is this:

By the middle of the 21st century, a team of fully autonomous humanoid robot soccer players shall win a soccer game, complying with the official rules of FIFA, against the winner of the most recent World Cup.

While the robots are certainly not there yet, they're definitely getting closer.

Even if you’re not a particular fan of soccer, it’s impressive to watch the robots coordinate with each other, setting up multiple passes and changing tactics on the fly in response to the movements of the other team. And the ability of these robots to shoot accurately is world-class (like, human world-class), as they’re seemingly able to put the ball in whatever corner of the goal they choose with split-second timing.

The final match was between Tech United from Eindhoven University of Technology in the Netherlands (whose robots are called TURTLE), and Team Water from Beijing Information Science & Technology University. Without spoiling it, I can tell you that the game was tied within just the last few seconds, meaning that it had to go to overtime. You can watch the entire match on YouTube, or a 5-minute commentated highlight video here:

It’s become a bit of a tradition to have the winning MSL robots play a team of what looks to be inexperienced adult humans wearing long pants and dress shoes.

The fact that the robots managed to score even once is pretty awesome, and it also looks like the robots are playing very conservatively (more so than the humans) so as not to accidentally injure any of us fragile meatbags with our spindly little legs. I get that RoboCup wants its first team of robots that can beat a human World Cup winning team to be humanoids, but at the moment, the MSL robots are where all the skill is.

To get calibrated on the state of the art for humanoid soccer robots, here’s the adult size final, Team Nimbro from the University of Bonn in Germany versus Team Sweaty from Offenburg University in Germany:

Yup, still a lot of falling over.

There’s lots more RoboCup on YouTube: Some channels to find more matches include the official RoboCup 2019 channel, and Tech United Eindhoven’s channel, which has both live English commentary and some highlight videos.

[ RoboCup 2019 ] Continue reading →

Posted in Human Robots