Tag Archives: Edge

#436186 Video Friday: Invasion of the Mini ...

Posted on November 21, 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!):

DARPA SubT Urban Circuit – February 18-27, 2020 – Olympia, Wash., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

There will be a Mini-Cheetah Workshop (sponsored by Naver Labs) a year from now at IROS 2020 in Las Vegas. Mini-Cheetahs for everyone!

That’s just a rendering, of course, but this isn’t:

[ MCW ]

I was like 95 percent sure that the Urban Circuit of the DARPA SubT Challenge was going to be in something very subway station-y. Oops!

In the Subterranean (SubT) Challenge, teams deploy autonomous ground and aerial systems to attempt to map, identify, and report artifacts along competition courses in underground environments. The artifacts represent items a first responder or service member may encounter in unknown underground sites. This video provides a preview of the Urban Circuit event location. The Urban Circuit is scheduled for February 18-27, 2020, at Satsop Business Park west of Olympia, Washington.

[ SubT ]

Researchers at SEAS and the Wyss Institute for Biologically Inspired Engineering have developed a resilient RoboBee powered by soft artificial muscles that can crash into walls, fall onto the floor, and collide with other RoboBees without being damaged. It is the first microrobot powered by soft actuators to achieve controlled flight.

To solve the problem of power density, the researchers built upon the electrically-driven soft actuators developed in the lab of David Clarke, the Extended Tarr Family Professor of Materials. These soft actuators are made using dielectric elastomers, soft materials with good insulating properties, that deform when an electric field is applied. By improving the electrode conductivity, the researchers were able to operate the actuator at 500 Hertz, on par with the rigid actuators used previously in similar robots.

Next, the researchers aim to increase the efficiency of the soft-powered robot, which still lags far behind more traditional flying robots.

[ Harvard ]

We present a system for fast and robust handovers with a robot character, together with a user study investigating the effect of robot speed and reaction time on perceived interaction quality. The system can match and exceed human speeds and confirms that users prefer human-level timing.

In a 3×3 user study, we vary the speed of the robot and add variable sensorimotor delays. We evaluate the social perception of the robot using the Robot Social Attribute Scale (RoSAS). Inclusion of a small delay, mimicking the delay of the human sensorimotor system, leads to an improvement in perceived qualities over both no delay and long delay conditions. Specifically, with no delay the robot is perceived as more discomforting and with a long delay, it is perceived as less warm.

[ Disney Research ]

When cars are autonomous, they’re not going to be able to pump themselves full of gas. Or, more likely, electrons. Kuka has the solution.

[ Kuka ]

This looks like fun, right?

[ Robocoaster ]

NASA is leading the way in the use of On-orbit Servicing, Assembly, and Manufacturing to enable large, persistent, upgradable, and maintainable spacecraft. This video was developed by the Advanced Concepts Lab (ACL) at NASA Langley Research Center.

[ NASA ]

The noisiest workshop by far at Humanoids last month (by far) was Musical Interactions With Humanoids, the end result of which was this:

[ Workshop ]

IROS is an IEEE event, and in furthering the IEEE mission to benefit humanity through technological innovation, IROS is doing a great job. But don’t take it from us – we are joined by IEEE President-Elect Professor Toshio Fukuda to find out a bit more about the impact events like IROS can have, as well as examine some of the issues around intelligent robotics and systems – from privacy to transparency of the systems at play.

[ IROS ]

Speaking of IROS, we hope you’ve been enjoying our coverage. We have already featured Harvard’s strange sea-urchin-inspired robot and a Japanese quadruped that can climb vertical ladders, with more stories to come over the next several weeks.

In the mean time, enjoy these 10 videos from the conference (as usual, we’re including the title, authors, and abstract for each—if you’d like more details about any of these projects, let us know and we’ll find out more for you).

“A Passive Closing, Tendon Driven, Adaptive Robot Hand for Ultra-Fast, Aerial Grasping and Perching,” by Andrew McLaren, Zak Fitzgerald, Geng Gao, and Minas Liarokapis from the University of Auckland, New Zealand.

Current grasping methods for aerial vehicles are slow, inaccurate and they cannot adapt to any target object. Thus, they do not allow for on-the-fly, ultra-fast grasping. In this paper, we present a passive closing, adaptive robot hand design that offers ultra-fast, aerial grasping for a wide range of everyday objects. We investigate alternative uses of structural compliance for the development of simple, adaptive robot grippers and hands and we propose an appropriate quick release mechanism that facilitates an instantaneous grasping execution. The quick release mechanism is triggered by a simple distance sensor. The proposed hand utilizes only two actuators to control multiple degrees of freedom over three fingers and it retains the superior grasping capabilities of adaptive grasping mechanisms, even under significant object pose or other environmental uncertainties. The hand achieves a grasping time of 96 ms, a maximum grasping force of 56 N and it is able to secure objects of various shapes at high speeds. The proposed hand can serve as the end-effector of grasping capable Unmanned Aerial Vehicle (UAV) platforms and it can offer perching capabilities, facilitating autonomous docking.

“Unstructured Terrain Navigation and Topographic Mapping With a Low-Cost Mobile Cuboid Robot,” by Andrew S. Morgan, Robert L. Baines, Hayley McClintock, and Brian Scassellati from Yale University, USA.

Current robotic terrain mapping techniques require expensive sensor suites to construct an environmental representation. In this work, we present a cube-shaped robot that can roll through unstructured terrain and construct a detailed topographic map of the surface that it traverses in real time with low computational and monetary expense. Our approach devolves many of the complexities of locomotion and mapping to passive mechanical features. Namely, rolling movement is achieved by sequentially inflating latex bladders that are located on four sides of the robot to destabilize and tip it. Sensing is achieved via arrays of fine plastic pins that passively conform to the geometry of underlying terrain, retracting into the cube. We developed a topography by shade algorithm to process images of the displaced pins to reconstruct terrain contours and elevation. We experimentally validated the efficacy of the proposed robot through object mapping and terrain locomotion tasks.

“Toward a Ballbot for Physically Leading People: A Human-Centered Approach,” by Zhongyu Li and Ralph Hollis from Carnegie Mellon University, USA.

This work presents a new human-centered method for indoor service robots to provide people with physical assistance and active guidance while traveling through congested and narrow spaces. As most previous work is robot-centered, this paper develops an end-to-end framework which includes a feedback path of the measured human positions. The framework combines a planning algorithm and a human-robot interaction module to guide the led person to a specified planned position. The approach is deployed on a person-size dynamically stable mobile robot, the CMU ballbot. Trials were conducted where the ballbot physically led a blindfolded person to safely navigate in a cluttered environment.

“Achievement of Online Agile Manipulation Task for Aerial Transformable Multilink Robot,” by Fan Shi, Moju Zhao, Tomoki Anzai, Keita Ito, Xiangyu Chen, Kei Okada, and Masayuki Inaba from the University of Tokyo, Japan.

Transformable aerial robots are favorable in aerial manipulation tasks for their flexible ability to change configuration during the flight. By assuming robot keeping in the mild motion, the previous researches sacrifice aerial agility to simplify the complex non-linear system into a single rigid body with a linear controller. In this paper, we present a framework towards agile swing motion for the transformable multi-links aerial robot. We introduce a computational-efficient non-linear model predictive controller and joints motion primitive frame-work to achieve agile transforming motions and validate with a novel robot named HYRURS-X. Finally, we implement our framework under a table tennis task to validate the online and agile performance.

“Small-Scale Compliant Dual Arm With Tail for Winged Aerial Robots,” by Alejandro Suarez, Manuel Perez, Guillermo Heredia, and Anibal Ollero from the University of Seville, Spain.

Winged aerial robots represent an evolution of aerial manipulation robots, replacing the multirotor vehicles by fixed or flapping wing platforms. The development of this morphology is motivated in terms of efficiency, endurance and safety in some inspection operations where multirotor platforms may not be suitable. This paper presents a first prototype of compliant dual arm as preliminary step towards the realization of a winged aerial robot capable of perching and manipulating with the wings folded. The dual arm provides 6 DOF (degrees of freedom) for end effector positioning in a human-like kinematic configuration, with a reach of 25 cm (half-scale w.r.t. the human arm), and 0.2 kg weight. The prototype is built with micro metal gear motors, measuring the joint angles and the deflection with small potentiometers. The paper covers the design, electronics, modeling and control of the arms. Experimental results in test-bench validate the developed prototype and its functionalities, including joint position and torque control, bimanual grasping, the dynamic equilibrium with the tail, and the generation of 3D maps with laser sensors attached at the arms.

“A Novel Small-Scale Turtle-inspired Amphibious Spherical Robot,” by Huiming Xing, Shuxiang Guo, Liwei Shi, Xihuan Hou, Yu Liu, Huikang Liu, Yao Hu, Debin Xia, and Zan Li from Beijing Institute of Technology, China.

This paper describes a novel small-scale turtle-inspired Amphibious Spherical Robot (ASRobot) to accomplish exploration tasks in the restricted environment, such as amphibious areas and narrow underwater cave. A Legged, Multi-Vectored Water-Jet Composite Propulsion Mechanism (LMVWCPM) is designed with four legs, one of which contains three connecting rod parts, one water-jet thruster and three joints driven by digital servos. Using this mechanism, the robot is able to walk like amphibious turtles on various terrains and swim flexibly in submarine environment. A simplified kinematic model is established to analyze crawling gaits. With simulation of the crawling gait, the driving torques of different joints contributed to the choice of servos and the size of links of legs. Then we also modeled the robot in water and proposed several underwater locomotion. In order to assess the performance of the proposed robot, a series of experiments were carried out in the lab pool and on flat ground using the prototype robot. Experiments results verified the effectiveness of LMVWCPM and the amphibious control approaches.

“Advanced Autonomy on a Low-Cost Educational Drone Platform,” by Luke Eller, Theo Guerin, Baichuan Huang, Garrett Warren, Sophie Yang, Josh Roy, and Stefanie Tellex from Brown University, USA.

PiDrone is a quadrotor platform created to accompany an introductory robotics course. Students build an autonomous flying robot from scratch and learn to program it through assignments and projects. Existing educational robots do not have significant autonomous capabilities, such as high-level planning and mapping. We present a hardware and software framework for an autonomous aerial robot, in which all software for autonomy can run onboard the drone, implemented in Python. We present an Unscented Kalman Filter (UKF) for accurate state estimation. Next, we present an implementation of Monte Carlo (MC) Localization and Fast-SLAM for Simultaneous Localization and Mapping (SLAM). The performance of UKF, localization, and SLAM is tested and compared to ground truth, provided by a motion-capture system. Our evaluation demonstrates that our autonomous educational framework runs quickly and accurately on a Raspberry Pi in Python, making it ideal for use in educational settings.

“FlightGoggles: Photorealistic Sensor Simulation for Perception-driven Robotics using Photogrammetry and Virtual Reality,” by Winter Guerra, Ezra Tal, Varun Murali, Gilhyun Ryou and Sertac Karaman from the Massachusetts Institute of Technology, USA.

FlightGoggles is a photorealistic sensor simulator for perception-driven robotic vehicles. The key contributions of FlightGoggles are twofold. First, FlightGoggles provides photorealistic exteroceptive sensor simulation using graphics assets generated with photogrammetry. Second, it provides the ability to combine (i) synthetic exteroceptive measurements generated in silico in real time and (ii) vehicle dynamics and proprioceptive measurements generated in motio by vehicle(s) in flight in a motion-capture facility. FlightGoggles is capable of simulating a virtual-reality environment around autonomous vehicle(s) in flight. While a vehicle is in flight in the FlightGoggles virtual reality environment, exteroceptive sensors are rendered synthetically in real time while all complex dynamics are generated organically through natural interactions of the vehicle. The FlightGoggles framework allows for researchers to accelerate development by circumventing the need to estimate complex and hard-to-model interactions such as aerodynamics, motor mechanics, battery electrochemistry, and behavior of other agents. The ability to perform vehicle-in-the-loop experiments with photorealistic exteroceptive sensor simulation facilitates novel research directions involving, e.g., fast and agile autonomous flight in obstacle-rich environments, safe human interaction, and flexible sensor selection. FlightGoggles has been utilized as the main test for selecting nine teams that will advance in the AlphaPilot autonomous drone racing challenge. We survey approaches and results from the top AlphaPilot teams, which may be of independent interest. FlightGoggles is distributed as open-source software along with the photorealistic graphics assets for several simulation environments, under the MIT license at http://flightgoggles.mit.edu.

“An Autonomous Quadrotor System for Robust High-Speed Flight Through Cluttered Environments Without GPS,” by Marc Rigter, Benjamin Morrell, Robert G. Reid, Gene B. Merewether, Theodore Tzanetos, Vinay Rajur, KC Wong, and Larry H. Matthies from University of Sydney, Australia; NASA Jet Propulsion Laboratory, California Institute of Technology, USA; and Georgia Institute of Technology, USA.

Robust autonomous flight without GPS is key to many emerging drone applications, such as delivery, search and rescue, and warehouse inspection. These and other appli- cations require accurate trajectory tracking through cluttered static environments, where GPS can be unreliable, while high- speed, agile, flight can increase efficiency. We describe the hardware and software of a quadrotor system that meets these requirements with onboard processing: a custom 300 mm wide quadrotor that uses two wide-field-of-view cameras for visual- inertial motion tracking and relocalization to a prior map. Collision-free trajectories are planned offline and tracked online with a custom tracking controller. This controller includes compensation for drag and variability in propeller performance, enabling accurate trajectory tracking, even at high speeds where aerodynamic effects are significant. We describe a system identification approach that identifies quadrotor-specific parameters via maximum likelihood estimation from flight data. Results from flight experiments are presented, which 1) validate the system identification method, 2) show that our controller with aerodynamic compensation reduces tracking error by more than 50% in both horizontal flights at up to 8.5 m/s and vertical flights at up to 3.1 m/s compared to the state-of-the-art, and 3) demonstrate our system tracking complex, aggressive, trajectories.

“Morphing Structure for Changing Hydrodynamic Characteristics of a Soft Underwater Walking Robot,” by Michael Ishida, Dylan Drotman, Benjamin Shih, Mark Hermes, Mitul Luhar, and Michael T. Tolley from the University of California, San Diego (UCSD) and University of Southern California, USA.

Existing platforms for underwater exploration and inspection are often limited to traversing open water and must expend large amounts of energy to maintain a position in flow for long periods of time. Many benthic animals overcome these limitations using legged locomotion and have different hydrodynamic profiles dictated by different body morphologies. This work presents an underwater legged robot with soft legs and a soft inflatable morphing body that can change shape to influence its hydrodynamic characteristics. Flow over the morphing body separates behind the trailing edge of the inflated shape, so whether the protrusion is at the front, center, or back of the robot influences the amount of drag and lift. When the legged robot (2.87 N underwater weight) needs to remain stationary in flow, an asymmetrically inflated body resists sliding by reducing lift on the body by 40% (from 0.52 N to 0.31 N) at the highest flow rate tested while only increasing drag by 5.5% (from 1.75 N to 1.85 N). When the legged robot needs to walk with flow, a large inflated body is pushed along by the flow, causing the robot to walk 16% faster than it would with an uninflated body. The body shape significantly affects the ability of the robot to walk against flow as it is able to walk against 0.09 m/s flow with the uninflated body, but is pushed backwards with a large inflated body. We demonstrate that the robot can detect changes in flow velocity with a commercial force sensor and respond by morphing into a hydrodynamically preferable shape. Continue reading →

Posted in Human Robots

#436180 Bipedal Robot Cassie Cal Learns to ...

Posted on November 21, 2019 by Android

There’s no particular reason why knowing how to juggle would be a useful skill for a robot. Despite this, robots are frequently taught how to juggle things. Blind robots can juggle, humanoid robots can juggle, and even drones can juggle. Why? Because juggling is hard, man! You have to think about a bunch of different things at once, and also do a bunch of different things at once, which this particular human at least finds to be overly stressful. While juggling may not stress robots out, it does require carefully coordinated sensing and computing and actuation, which means that it’s as good a task as any (and a more entertaining task than most) for testing the capabilities of your system.

UC Berkeley’s Cassie Cal robot, which consists of two legs and what could be called a torso if you were feeling charitable, has just learned to juggle by bouncing a ball on what would be her head if she had one of those. The idea is that if Cassie can juggle while balancing at the same time, she’ll be better able to do other things that require dynamic multitasking, too. And if that doesn’t work out, she’ll still be able to join the circus.

Cassie’s juggling is assisted by an external motion capture system that tracks the location of the ball, but otherwise everything is autonomous. Cassie is able to juggle the ball by leaning forwards and backwards, left and right, and moving up and down. She does this while maintaining her own balance, which is the whole point of this research—successfully executing two dynamic behaviors that may sometimes be at odds with one another. The end goal here is not to make a better juggling robot, but rather to explore dynamic multitasking, a skill that robots will need in order to be successful in human environments.

This work is from the Hybrid Robotics Lab at UC Berkeley, led by Koushil Sreenath, and is being done by Katherine Poggensee, Albert Li, Daniel Sotsaikich, Bike Zhang, and Prasanth Kotaru.

For a bit more detail, we spoke with Albert Li via email.

Image: UC Berkeley

UC Berkeley’s Cassie Cal getting ready to juggle.

IEEE Spectrum: What would be involved in getting Cassie to juggle without relying on motion capture?

Albert Li: Our motivation for starting off with motion capture was to first address the control challenge of juggling on a biped without worrying about implementing the perception. We actually do have a ball detector working on a camera, which would mean we wouldn’t have to rely on the motion capture system. However, we need to mount the camera in a way that it would provide the best upwards field of view, and we also have develop a reliable estimator. The estimator is particularly important because when the ball gets close enough to the camera, we actually can’t track the ball and have to assume our dynamic models describe its motion accurately enough until it bounces back up.

What keeps Cassie from juggling indefinitely?

There are a few factors that affect how long Cassie can sustain a juggle. While in simulation the paddle exhibits homogeneous properties like its stiffness and damping, in reality every surface has anisotropic contact properties. So, there are parts of the paddle which may be better for juggling than others (and importantly, react differently than modeled). These differences in contact are also exacerbated due to how the paddle is cantilevered when mounted on Cassie. When the ball hits these areas, it leads to a larger than expected error in a juggle. Due to the small size of the paddle, the ball may then just hit the paddle’s edge and end the juggling run. Over a very long run, this is a likely occurrence. Additionally, some large juggling errors could cause Cassie’s feet to slip slightly, which ends up changing the stable standing position over time. Since this version of the controller assumes Cassie is stationary, this change in position eventually leads to poor juggles and failure.

Would Cassie be able to juggle while walking (or hovershoe-ing)?

Walking (and hovershoe-ing) while juggling is a far more challenging problem and is certainly a goal for future research. Some of these challenges include getting the paddle to precise poses to juggle the ball while also moving to avoid any destabilizing effects of stepping incorrectly. The number of juggles per step of walking could also vary and make the mathematics of the problem more challenging. The controller goal is also more involved. While the current goal of the juggling controller is to juggle the ball to a static apex position, with a walking juggling controller, we may instead want to hit the ball forwards and also walk forwards to bounce it, juggle the ball along a particular path, etc. Solving such challenges would be the main thrusts of the follow-up research.

Can you give an example of a practical task that would be made possible by using a controller like this?

Studying juggling means studying contact behavior and leveraging our models of it to achieve a known objective. Juggling could also be used to study predictable post-contact flight behavior. Consider the scenario where a robot is attempting to make a catch, but fails, letting the ball to bounce off of its hand, and then recovering the catch. This behavior could also be intentional: It is often easier to first execute a bounce to direct the target and then perform a subsequent action. For example, volleyball players could in principle directly hit a spiked ball back, but almost always bump the ball back up and then return it.

Even beyond this motivating example, the kinds of models we employ to get juggling working are more generally applicable to any task that involves contact, which could include tasks besides bouncing like sliding and rolling. For example, clearing space on a desk by pushing objects to the side may be preferable than individually manipulating each and every object on it.

You mention collaborative juggling or juggling multiple balls—is that something you’ve tried yet? Can you talk a bit more about what you’re working on next?

We haven’t yet started working on collaborative or multi-ball juggling, but that’s also a goal for future work. Juggling multiple balls statically is probably the most reasonable next goal, but presents additional challenges. For instance, you have to encode a notion of juggling urgency (if the second ball isn’t hit hard enough, you have less time to get the first ball up before you get back to the second one).

On the other hand, collaborative human-robot juggling requires a more advanced decision-making framework. To get robust multi-agent juggling, the robot will need to employ some sort of probabilistic model of the expected human behavior (are they likely to move somewhere? Are they trying to catch the ball high or low? Is it safe to hit the ball back?). In general, developing such human models is difficult since humans are fairly unpredictable and often don’t exhibit rational behavior. This will be a focus of future work.

[ Hybrid Robotics Lab ] Continue reading →

Posted in Human Robots

#436151 Natural Language Processing Dates Back ...

Posted on October 29, 2019 by Android

This is part one of a six-part series on the history of natural language processing.

We’re in the middle of a boom time for natural language processing (NLP), the field of computer science that focuses on linguistic interactions between humans and machines. Thanks to advances in machine learning over the past decade, we’ve seen vast improvements in speech recognition and machine translation software. Language generators are now good enough to write coherent news articles, and virtual agents like Siri and Alexa are becoming part of our daily lives.

Most trace the origins of this field back to the beginning of the computer age, when Alan Turing, writing in 1950, imagined a smart machine that could interact fluently with a human via typed text on a screen. For this reason, machine-generated language is mostly understood as a digital phenomenon—and a central goal of artificial intelligence (AI) research.

This six-part series will challenge that common understanding of NLP. In fact, attempts to design formal rules and machines that can analyze, process, and generate language go back hundreds of years.

Attempts to design formal rules and machines that can analyze, process, and generate language go back hundreds of years.

While specific technologies have changed over time, the basic idea of treating language as a material that can be artificially manipulated by rule-based systems has been pursued by many people in many cultures and for many different reasons. These historical experiments reveal the promise and perils of attempting to simulate human language in non-human ways—and they hold lessons for today’s practitioners of cutting-edge NLP techniques.

The story begins in medieval Spain. In the late 1200s, a Jewish mystic by the name of Abraham Abulafia sat down at a table in his small house in Barcelona, picked up a quill, dipped it in ink, and began combining the letters of the Hebrew alphabet in strange and seemingly random ways. Aleph with Bet, Bet with Gimmel, Gimmel with Aleph and Bet, and so on.

Abulafia called this practice “the science of the combination of letters.” He wasn’t actually combining letters at random; instead he was carefully following a secret set of rules that he had devised while studying an ancient Kabbalistic text called the Sefer Yetsirah. This book describes how God created “all that is formed and all that is spoken” by combining Hebrew letters according to sacred formulas. In one section, God exhausts all possible two-letter combinations of the 22 Hebrew letters.

By studying the Sefer Yetsirah, Abulafia gained the insight that linguistic symbols can be manipulated with formal rules in order to create new, interesting, insightful sentences. To this end, he spent months generating thousands of combinations of the 22 letters of the Hebrew alphabet and eventually emerged with a series of books that he claimed were endowed with prophetic wisdom.

For Abulafia, generating language according to divine rules offered insight into the sacred and the unknown, or as he put it, allowed him to “grasp things which by human tradition or by thyself thou would not be able to know.”

Combining letters to generate language allows thou to “grasp things which by human tradition or by thyself thou would not be able to know.”
—Abraham Abulafia, mystic

But other Jewish scholars considered this rudimentary language generation a dangerous act that bordered on the profane. The Talmud tells stories of rabbis who, by the magical act of permuting language according to the formulas set out in the Sefer Yetsirah, created artificial creatures called golems. In these tales, rabbis manipulated the letters of the Hebrew alphabet to replicate God’s act of creation, using the sacred formulas to imbue inanimate objects with life.

In some of these myths, the rabbis used this skill for practical reasons, to make animals to eat when hungry or servants to help them with domestic duties. But many of these golem stories end badly. In one particularly well-known fable, Judah Loew ben Bezalel, the 16th century rabbi of Prague, used the sacred practice of letter combinatorics to conjure a golem to protect the Jewish community from antisemitic attacks, only to see the golem turn violently on him instead.

This “science of the combination of letters” was a rudimentary form of natural language processing, as it involved combining letters of the Hebrew alphabet according to specific rules. For Kabbalists, it was a double-edged sword: a way to access new forms of knowledge and wisdom, but also an inherently dangerous practice that could bring about unintended consequences.

This tension reappears throughout the long history of language processing, and still echoes in discussions about the most cutting-edge NLP technology of our digital era.

This is the first installment of a six-part series on the history of natural language processing. Come back next Monday for part two, “In the 17th Century, Leibniz Dreamed of a Machine That Could Calculate Ideas​.”

You can also check out our prior series on the untold history of AI. Continue reading →

Posted in Human Robots

#435804 New AI Systems Are Here to Personalize ...

Posted on September 24, 2019 by Android

The narratives about automation and its impact on jobs go from urgent to hopeful and everything in between. Regardless where you land, it’s hard to argue against the idea that technologies like AI and robotics will change our economy and the nature of work in the coming years.

A recent World Economic Forum report noted that some estimates show automation could displace 75 million jobs by 2022, while at the same time creating 133 million new roles. While these estimates predict a net positive for the number of new jobs in the coming decade, displaced workers will need to learn new skills to adapt to the changes. If employees can’t be retrained quickly for jobs in the changing economy, society is likely to face some degree of turmoil.

According to Bryan Talebi, CEO and founder of AI education startup Ahura AI, the same technologies erasing and creating jobs can help workers bridge the gap between the two.

Ahura is developing a product to capture biometric data from adult learners who are using computers to complete online education programs. The goal is to feed this data to an AI system that can modify and adapt their program to optimize for the most effective teaching method.

While the prospect of a computer recording and scrutinizing a learner’s behavioral data will surely generate unease across a society growing more aware and uncomfortable with digital surveillance, some people may look past such discomfort if they experience improved learning outcomes. Users of the system would, in theory, have their own personalized instruction shaped specifically for their unique learning style.

And according to Talebi, their systems are showing some promise.

“Based on our early tests, our technology allows people to learn three to five times faster than traditional education,” Talebi told me.

Currently, Ahura’s system uses the video camera and microphone that come standard on the laptops, tablets, and mobile devices most students are using for their learning programs.

With the computer’s camera Ahura can capture facial movements and micro expressions, measure eye movements, and track fidget score (a measure of how much a student moves while learning). The microphone tracks voice sentiment, and the AI leverages natural language processing to review the learner’s word usage.

From this collection of data Ahura can, according to Talebi, identify the optimal way to deliver content to each individual.

For some users that might mean a video tutorial is the best style of learning, while others may benefit more from some form of experiential or text-based delivery.

“The goal is to alter the format of the content in real time to optimize for attention and retention of the information,” said Talebi. One of Ahura’s main goals is to reduce the frequency with which students switch from their learning program to distractions like social media.

“We can now predict with a 60 percent confidence interval ten seconds before someone switches over to Facebook or Instagram. There’s a lot of work to do to get that up to a 95 percent level, so I don’t want to overstate things, but that’s a promising indication that we can work to cut down on the amount of context-switching by our students,” Talebi said.

Talebi repeatedly mentioned his ambition to leverage the same design principles used by Facebook, Twitter, and others to increase the time users spend on those platforms, but instead use them to design more compelling and even addictive education programs that can compete for attention with social media.

But the notion that Ahura’s system could one day be used to create compelling or addictive education necessarily presses against a set of justified fears surrounding data privacy. Growing anxiety surrounding the potential to misuse user data for social manipulation is widespread.

“Of course there is a real danger, especially because we are collecting so much data about our users which is specifically connected to how they consume content. And because we are looking so closely at the ways people interact with content, it’s incredibly important that this technology never be used for propaganda or to sell things to people,” Talebi tried to assure me.

Unsurprisingly (and worrying), using this AI system to sell products to people is exactly where some investors’ ambitions immediately turn once they learn about the company’s capabilities, according to Talebi. During our discussion Talebi regularly cited the now infamous example of Cambridge Analytica, the political consulting firm hired by the Trump campaign to run a psychographically targeted persuasion campaign on the US population during the most recent presidential election.

“It’s important that we don’t use this technology in those ways. We’re aware that things can go sideways, so we’re hoping to put up guardrails to ensure our system is helping and not harming society,” Talebi said.

Talebi will surely need to take real action on such a claim, but says the company is in the process of identifying a structure for an ethics review board—one that carries significant influence with similar voting authority as the executive team and the regular board.

“Our goal is to build an ethics review board that has teeth, is diverse in both gender and background but also in thought and belief structures. The idea is to have our ethics review panel ensure we’re building things ethically,” he said.

Data privacy appears to be an important issue for Talebi, who occasionally referenced a major competitor in the space based in China. According to a recent article from MIT Tech Review outlining the astonishing growth of AI-powered education platforms in China, data privacy concerns may be less severe there than in the West.

Ahura is currently developing upgrades to an early alpha-stage prototype, but is already capturing data from students from at least one Ivy League school and a variety of other places. Their next step is to roll out a working beta version to over 200,000 users as part of a partnership with an unnamed corporate client who will be measuring the platform’s efficacy against a control group.

Going forward, Ahura hopes to add to its suite of biometric data capture by including things like pupil dilation and facial flushing, heart rate, sleep patterns, or whatever else may give their system an edge in improving learning outcomes.

As information technologies increasingly automate work, it’s likely we’ll also see rapid changes to our labor systems. It’s also looking increasingly likely that those same technologies will be used to improve our ability to give people the right skills when they need them. It may be one way to address the challenges automation is sure to bring.

Image Credit: Gerd Altmann / Pixabay Continue reading →

Posted in Human Robots

#435775 Jaco Is a Low-Power Robot Arm That Hooks ...

Posted on September 20, 2019 by Android

We usually think of robots as taking the place of humans in various tasks, but robots of all kinds can also enhance human capabilities. This may be especially true for people with disabilities. And while the Cybathlon competition showed what's possible when cutting-edge research robotics is paired with expert humans, that competition isn't necessarily reflective of the kind of robotics available to most people today.

Kinova Robotics's Jaco arm is an assistive robotic arm designed to be mounted on an electric wheelchair. With six degrees of freedom plus a three-fingered gripper, the lightweight carbon fiber arm is frequently used in research because it's rugged and versatile. But from the start, Kinova created it to add autonomy to the lives of people with mobility constraints.

Earlier this year, Kinova shared the story of Mary Nelson, an 11-year-old girl with spinal muscular atrophy, who uses her Jaco arm to show her horse in competition. Spinal muscular atrophy is a neuromuscular disorder that impairs voluntary muscle movement, including muscles that help with respiration, and Mary depends on a power chair for mobility.

We wanted to learn more about how Kinova designs its Jaco arm, and what that means for folks like Mary, so we spoke with both Kinova and Mary's parents to find out how much of a difference a robot arm can make.

IEEE Spectrum: How did Mary interact with the world before having her arm, and what was involved in the decision to try a robot arm in general? And why then Kinova's arm specifically?

Ryan Nelson: Mary interacts with the world much like you and I do, she just uses different tools to do so. For example, she is 100 percent independent using her computer, iPad, and phone, and she prefers to use a mouse. However, she cannot move a standard mouse, so she connects her wheelchair to each device with Bluetooth to move the mouse pointer/cursor using her wheelchair joystick.

For years, we had a Manfrotto magic arm and super clamp attached to her wheelchair and she used that much like the robotic arm. We could put a baseball bat, paint brush, toys, etc. in the super clamp so that Mary could hold the object and interact as physically able children do. Mary has always wanted to be more independent, so we knew the robotic arm was something she must try. We had seen videos of the Kinova arm on YouTube and on their website, so we reached out to them to get a trial.

Can you tell us about the Jaco arm, and how the process of designing an assistive robot arm is different from the process of designing a conventional robot arm?

Nathaniel Swenson, Director of U.S. Operations — Assistive Technologies at Kinova: Jaco is our flagship robotic arm. Inspired by our CEO's uncle and its namesake, Jacques “Jaco” Forest, it was designed as assistive technology with power wheelchair users in mind.

The primary differences between Jaco and our other robots, such as the new Gen3, which was designed to meet the needs of academic and industry research teams, are speed and power consumption. Other robots such as the Gen3 can move faster and draw slightly more power because they aren't limited by the battery size of power wheelchairs. Depending on the use case, they might not interact directly with a human being in the research setting and can safely move more quickly. Jaco is designed to move at safe speeds and make direct contact with the end user and draw very little power directly from their wheelchair.

The most important consideration in the design process of an assistive robot is the safety of the end user. Jaco users operate their robots through their existing drive controls to assist them in daily activities such as eating, drinking, and opening doors and they don't have to worry about the robot draining their chair's batteries throughout the day. The elegant design that results from meeting the needs of our power chair users has benefited subsequent iterations, [of products] such as the Gen3, as well: Kinova's robots are lightweight, extremely efficient in their power consumption, and safe for direct human-robot interaction. This is not true of conventional industrial robots.

What was the learning process like for Mary? Does she feel like she's mastered the arm, or is it a continuous learning process?

Ryan Nelson: The learning process was super quick for Mary. However, she amazes us every day with the new things that she can do with the arm. Literally within minutes of installing the arm on her chair, Mary had it figured out and was shaking hands with the Kinova rep. The control of the arm is super intuitive and the Kinova reps say that SMA (Spinal Muscular Atrophy) children are perfect users because they are so smart—they pick it up right away. Mary has learned to do many fine motor tasks with the arm, from picking up small objects like a pencil or a ruler, to adjusting her glasses on her face, to doing science experiments.

Photo: The Nelson Family

Mary uses a headset microphone to amplify her voice, and she will use the arm and finger to adjust the microphone in front of her mouth after she is done eating (also a task she mastered quickly with the arm). Additionally, Mary will use the arms to reach down and adjust her feet or leg by grabbing them with the arm and moving them to a more comfortable position. All of these examples are things she never really asked us to do, but something she needed and just did on her own, with the help of the arm.

What is the most common feedback that you get from new users of the arm? How about from experienced users who have been using the arm for a while?

Nathaniel Swenson: New users always tell us how excited they are to see what they can accomplish with their new Jaco. From day one, they are able to do things that they have longed to do without assistance from a caregiver: take a drink of water or coffee, scratch an itch, push the button to open an “accessible” door or elevator, or even feed their baby with a bottle.

The most common feedback I hear from experienced users is that Jaco has changed their life. Our experienced users like Mary are rock stars: everywhere they go, people get excited to see what they'll do next. The difference between a new user and an experienced user could be as little as two weeks. People who operate power wheelchairs every day are already expert drivers and we just add a new “gear” to their chair: robot mode. It's fun to see how quickly new users master the intuitive Jaco control modes.

What changes would you like to see in the next generation of Jaco arm?

Ryan Nelson: Titanium fingers! Make it lift heavier objects, hold heavier items like a baseball bat, machine gun, flame thrower, etc., and Mary literally said this last night: “I wish the arm moved fast enough to play the piano.”

Nathaniel Swenson: I love the idea of titanium fingers! Jaco's fingers are made from a flexible polymer and designed to avoid harm. This allows the fingers to bend or dislocate, rather than break, but it also means they are not as durable as a material like titanium. Increased payload, the ability to manipulate heavier objects, requires increased power consumption. We've struck a careful balance between providing enough strength to accomplish most medically necessary Activities of Daily Living and efficient use of the power chair's batteries.

We take Isaac Asimov's Laws of Robotics pretty seriously. When we start to combine machine guns, flame throwers, and artificial intelligence with robots, I get very nervous!

I wish the arm moved fast enough to play the piano, too! I am also a musician and I share Mary's dream of an assistive robot that would enable her to make music. In the meantime, while we work on that, please enjoy this beautiful violin piece by Manami Ito and her one-of-a-kind violin prosthesis:

To what extent could more autonomy for the arm be helpful for users? What would be involved in implementing that?

Nathaniel Swenson: Artificial intelligence, machine learning, and deep learning will introduce greater autonomy in future iterations of assistive robots. This will enable them to perform more complex tasks that aren't currently possible, and enable them to accomplish routine tasks more quickly and with less input than the current manual control requires.

For assistive robots, implementation of greater autonomy involves a focus on end-user safety and improvements in the robot's awareness of its environment. Autonomous robots that work in close proximity with humans need vision. They must be able to see to avoid collisions and they use haptic feedback to tell the robot how much force is being exerted on objects. All of these technologies exist, but the largest obstacle to bringing them to the assistive technology market is to prove to the health insurance companies who will fund them that they are both safe and medically necessary. Continue reading →

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