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#436126 Quantum Computing Gets a Boost From AI ...

Posted on October 22, 2019 by Android

Illustration: Greg Mably

Anyone of a certain age who has even a passing interest in computers will remember the remarkable breakthrough that IBM made in 1997 when its Deep Blue chess-playing computer defeated Garry Kasparov, then the world chess champion. Computer scientists passed another such milestone in March 2016, when DeepMind (a subsidiary of Alphabet, Google’s parent company) announced that its AlphaGo program had defeated world-champion player Lee Sedol in the game of Go, a board game that had vexed AI researchers for decades. Recently, DeepMind’s algorithms have also bested human players in the computer games StarCraft IIand Quake Arena III.

Some believe that the cognitive capacities of machines will overtake those of human beings in many spheres within a few decades. Others are more cautious and point out that our inability to understand the source of our own cognitive powers presents a daunting hurdle. How can we make thinking machines if we don’t fully understand our own thought processes?

Citizen science, which enlists masses of people to tackle research problems, holds promise here, in no small part because it can be used effectively to explore the boundary between human and artificial intelligence.

Some citizen-science projects ask the public to collect data from their surroundings (as eButterfly does for butterflies) or to monitor delicate ecosystems (as Eye on the Reef does for Australia’s Great Barrier Reef). Other projects rely on online platforms on which people help to categorize obscure phenomena in the night sky (Zooniverse) or add to the understanding of the structure of proteins (Foldit). Typically, people can contribute to such projects without any prior knowledge of the subject. Their fundamental cognitive skills, like the ability to quickly recognize patterns, are sufficient.

In order to design and develop video games that can allow citizen scientists to tackle scientific problems in a variety of fields, professor and group leader Jacob Sherson founded ScienceAtHome (SAH), at Aarhus University, in Denmark. The group began by considering topics in quantum physics, but today SAH hosts games covering other areas of physics, math, psychology, cognitive science, and behavioral economics. We at SAH search for innovative solutions to real research challenges while providing insight into how people think, both alone and when working in groups.

It is computationally intractable to completely map out a higher-dimensional landscape: It is called the curse of high dimensionality, and it plagues many optimization problems.

We believe that the design of new AI algorithms would benefit greatly from a better understanding of how people solve problems. This surmise has led us to establish the Center for Hybrid Intelligence within SAH, which tries to combine human and artificial intelligence, taking advantage of the particular strengths of each. The center’s focus is on the gamification of scientific research problems and the development of interfaces that allow people to understand and work together with AI.

Our first game, Quantum Moves, was inspired by our group’s research into quantum computers. Such computers can in principle solve certain problems that would take a classical computer billions of years. Quantum computers could challenge current cryptographic protocols, aid in the design of new materials, and give insight into natural processes that require an exact solution of the equations of quantum mechanics—something normal computers are inherently bad at doing.

One candidate system for building such a computer would capture individual atoms by “freezing” them, as it were, in the interference pattern produced when a laser beam is reflected back on itself. The captured atoms can thus be organized like eggs in a carton, forming a periodic crystal of atoms and light. Using these atoms to perform quantum calculations requires that we use tightly focused laser beams, called optical tweezers, to transport the atoms from site to site in the light crystal. This is a tricky business because individual atoms do not behave like particles; instead, they resemble a wavelike liquid governed by the laws of quantum mechanics.

In Quantum Moves, a player manipulates a touch screen or mouse to move a simulated laser tweezer and pick up a trapped atom, represented by a liquidlike substance in a bowl. Then the player must bring the atom back to the tweezer’s initial position while trying to minimize the sloshing of the liquid. Such sloshing would increase the energy of the atom and ultimately introduce errors into the operations of the quantum computer. Therefore, at the end of a move, the liquid should be at a complete standstill.

To understand how people and computers might approach such a task differently, you need to know something about how computerized optimization algorithms work. The countless ways of moving a glass of water without spilling may be regarded as constituting a “solution landscape.” One solution is represented by a single point in that landscape, and the height of that point represents the quality of the solution—how smoothly and quickly the glass of water was moved. This landscape might resemble a mountain range, where the top of each mountain represents a local optimum and where the challenge is to find the highest peak in the range—the global optimum.

Illustration: Greg Mably

Researchers must compromise between searching the landscape for taller mountains (“exploration”) and climbing to the top of the nearest mountain (“exploitation”). Making such a trade-off may seem easy when exploring an actual physical landscape: Merely hike around a bit to get at least the general lay of the land before surveying in greater detail what seems to be the tallest peak. But because each possible way of changing the solution defines a new dimension, a realistic problem can have thousands of dimensions. It is computationally intractable to completely map out such a higher-dimensional landscape. We call this the curse of high dimensionality, and it plagues many optimization problems.

Although algorithms are wonderfully efficient at crawling to the top of a given mountain, finding good ways of searching through the broader landscape poses quite a challenge, one that is at the forefront of AI research into such control problems. The conventional approach is to come up with clever ways of reducing the search space, either through insights generated by researchers or with machine-learning algorithms trained on large data sets.

At SAH, we attacked certain quantum-optimization problems by turning them into a game. Our goal was not to show that people can beat computers in this arena but rather to understand the process of generating insights into such problems. We addressed two core questions: whether allowing players to explore the infinite space of possibilities will help them find good solutions and whether we can learn something by studying their behavior.

Today, more than 250,000 people have played Quantum Moves, and to our surprise, they did in fact search the space of possible moves differently from the algorithm we had put to the task. Specifically, we found that although players could not solve the optimization problem on their own, they were good at searching the broad landscape. The computer algorithms could then take those rough ideas and refine them.

Herbert A. Simon said that “solving a problem simply means representing it so as to make the solution transparent.” Apparently, that’s what our games can do with their novel user interfaces.

Perhaps even more interesting was our discovery that players had two distinct ways of solving the problem, each with a clear physical interpretation. One set of players started by placing the tweezer close to the atom while keeping a barrier between the atom trap and the tweezer. In classical physics, a barrier is an impenetrable obstacle, but because the atom liquid is a quantum-mechanical object, it can tunnel through the barrier into the tweezer, after which the player simply moved the tweezer to the target area. Another set of players moved the tweezer directly into the atom trap, picked up the atom liquid, and brought it back. We called these two strategies the “tunneling” and “shoveling” strategies, respectively.

Such clear strategies are extremely valuable because they are very difficult to obtain directly from an optimization algorithm. Involving humans in the optimization loop can thus help us gain insight into the underlying physical phenomena that are at play, knowledge that may then be transferred to other types of problems.

Quantum Moves raised several obvious issues. First, because generating an exceptional solution required further computer-based optimization, players were unable to get immediate feedback to help them improve their scores, and this often left them feeling frustrated. Second, we had tested this approach on only one scientific challenge with a clear classical analogue, that of the sloshing liquid. We wanted to know whether such gamification could be applied more generally, to a variety of scientific challenges that do not offer such immediately applicable visual analogies.

We address these two concerns in Quantum Moves 2. Here, the player first generates a number of candidate solutions by playing the original game. Then the player chooses which solutions to optimize using a built-in algorithm. As the algorithm improves a player’s solution, it modifies the solution path—the movement of the tweezer—to represent the optimized solution. Guided by this feedback, players can then improve their strategy, come up with a new solution, and iteratively feed it back into this process. This gameplay provides high-level heuristics and adds human intuition to the algorithm. The person and the machine work in tandem—a step toward true hybrid intelligence.

In parallel with the development of Quantum Moves 2, we also studied how people collaboratively solve complex problems. To that end, we opened our atomic physics laboratory to the general public—virtually. We let people from around the world dictate the experiments we would run to see if they would find ways to improve the results we were getting. What results? That’s a little tricky to explain, so we need to pause for a moment and provide a little background on the relevant physics.

One of the essential steps in building the quantum computer along the lines described above is to create the coldest state of matter in the universe, known as a Bose-Einstein condensate. Here millions of atoms oscillate in synchrony to form a wavelike substance, one of the largest purely quantum phenomena known. To create this ultracool state of matter, researchers typically use a combination of laser light and magnetic fields. There is no familiar physical analogy between such a strange state of matter and the phenomena of everyday life.

The result we were seeking in our lab was to create as much of this enigmatic substance as was possible given the equipment available. The sequence of steps to accomplish that was unknown. We hoped that gamification could help to solve this problem, even though it had no classical analogy to present to game players.

Images: ScienceAtHome

Fun and Games: The
Quantum Moves game evolved over time, from a relatively crude early version [top] to its current form [second from top] and then a major revision,
Quantum Moves 2 [third from top].
Skill Lab: Science Detective games [bottom] test players’ cognitive skills.

In October 2016, we released a game that, for two weeks, guided how we created Bose-Einstein condensates in our laboratory. By manipulating simple curves in the game interface, players generated experimental sequences for us to use in producing these condensates—and they did so without needing to know anything about the underlying physics. A player would generate such a solution, and a few minutes later we would run the sequence in our laboratory. The number of ultracold atoms in the resulting Bose-Einstein condensate was measured and fed back to the player as a score. Players could then decide either to try to improve their previous solution or to copy and modify other players’ solutions. About 600 people from all over the world participated, submitting 7,577 solutions in total. Many of them yielded bigger condensates than we had previously produced in the lab.

So this exercise succeeded in achieving our primary goal, but it also allowed us to learn something about human behavior. We learned, for example, that players behave differently based on where they sit on the leaderboard. High-performing players make small changes to their successful solutions (exploitation), while poorly performing players are willing to make more dramatic changes (exploration). As a collective, the players nicely balance exploration and exploitation. How they do so provides valuable inspiration to researchers trying to understand human problem solving in social science as well as to those designing new AI algorithms.

How could mere amateurs outperform experienced experimental physicists? The players certainly weren’t better at physics than the experts—but they could do better because of the way in which the problem was posed. By turning the research challenge into a game, we gave players the chance to explore solutions that had previously required complex programming to study. Indeed, even expert experimentalists improved their solutions dramatically by using this interface.

Insight into why that’s possible can probably be found in the words of the late economics Nobel laureate Herbert A. Simon: “Solving a problem simply means representing it so as to make the solution transparent [PDF].” Apparently, that’s what our games can do with their novel user interfaces. We believe that such interfaces might be a key to using human creativity to solve other complex research problems.

Eventually, we’d like to get a better understanding of why this kind of gamification works as well as it does. A first step would be to collect more data on what the players do while they are playing. But even with massive amounts of data, detecting the subtle patterns underlying human intuition is an overwhelming challenge. To advance, we need a deeper insight into the cognition of the individual players.

As a step forward toward this goal, ScienceAtHome created Skill Lab: Science Detective, a suite of minigames exploring visuospatial reasoning, response inhibition, reaction times, and other basic cognitive skills. Then we compare players’ performance in the games with how well these same people did on established psychological tests of those abilities. The point is to allow players to assess their own cognitive strengths and weaknesses while donating their data for further public research.

In the fall of 2018 we launched a prototype of this large-scale profiling in collaboration with the Danish Broadcasting Corp. Since then more than 20,000 people have participated, and in part because of the publicity granted by the public-service channel, participation has been very evenly distributed across ages and by gender. Such broad appeal is rare in social science, where the test population is typically drawn from a very narrow demographic, such as college students.

Never before has such a large academic experiment in human cognition been conducted. We expect to gain new insights into many things, among them how combinations of cognitive abilities sharpen or decline with age, what characteristics may be used to prescreen for mental illnesses, and how to optimize the building of teams in our work lives.

And so what started as a fun exercise in the weird world of quantum mechanics has now become an exercise in understanding the nuances of what makes us human. While we still seek to understand atoms, we can now aspire to understand people’s minds as well.

This article appears in the November 2019 print issue as “A Man-Machine Mind Meld for Quantum Computing.”

About the Authors
Ottó Elíasson, Carrie Weidner, Janet Rafner, and Shaeema Zaman Ahmed work with the ScienceAtHome project at Aarhus University in Denmark. Continue reading →

Posted in Human Robots

#436094 Agility Robotics Unveils Upgraded Digit ...

Posted on October 14, 2019 by Android

Last time we saw Agility Robotics’ Digit biped, it was picking up a box from a Ford delivery van and autonomously dropping it off on a porch, while at the same time managing to not trip over stairs, grass, or small children. As a demo, it was pretty impressive, but of course there’s an enormous gap between making a video of a robot doing a successful autonomous delivery and letting that robot out into the semi-structured world and expecting it to reliably do a good job.

Agility Robotics is aware of this, of course, and over the last six months they’ve been making substantial improvements to Digit to make it more capable and robust. A new video posted today shows what’s new with the latest version of Digit—Digit v2.

We appreciate Agility Robotics foregoing music in the video, which lets us hear exactly what Digit sounds like in operation. The most noticeable changes are in Digit’s feet, torso, and arms, and I was particularly impressed to see Digit reposition the box on the table before grasping it to make sure that it could get a good grip. Otherwise, it’s hard to tell what’s new, so we asked Agility Robotics’ CEO Damion Shelton to get us up to speed.

IEEE Spectrum: Can you summarize the differences between Digit v1 and v2? We’re particularly interested in the new feet.

Damion Shelton: The feet now include a roll degree of freedom, so that Digit can resist lateral forces without needing to side step. This allows Digit v2 to balance on one foot statically, which Digit v1 and Cassie could not do. The larger foot also dramatically decreases load per unit area, for improved performance on very soft surfaces like sand.

The perception stack includes four Intel RealSense cameras used for obstacle detection and pick/place, plus the lidar. In Digit v1, the perception systems were brought up incrementally over time for development purposes. In Digit v2, all perception systems are active from the beginning and tied to a dedicated computer. The perception system is used for a number of additional things beyond manipulation, which we’ll start to show in the next few weeks.

The torso changes are a bit more behind-the-scenes. All of the electronics in it are now fully custom, thermally managed, and environmentally sealed. We’ve also included power and ethernet to a payload bay that can fit either a NUC or Jetson module (or other customer payload).

What exactly are we seeing in the video in terms of Digit’s autonomous capabilities?

At the moment this is a demonstration of shared autonomy. Picking and placing the box is fully autonomous. Balance and footstep placement are fully autonomous, but guidance and obstacle avoidance are under local teleop. It’s no longer a radio controller as in early videos; we’re not ready to reveal our current controller design but it’s a reasonably significant upgrade. This is v2 hardware, so there’s one more full version in development prior to the 2020 launch, which will expand the autonomy envelope significantly.

“This is a demonstration of shared autonomy. Picking and placing the box is fully autonomous. Balance and footstep placement are fully autonomous, but guidance and obstacle avoidance are under local teleop. It’s no longer a radio controller as in early videos; we’re not ready to reveal our current controller design but it’s a reasonably significant upgrade”
—Damion Shelton, Agility Robotics

What are some unique features or capabilities of Digit v2 that might not be obvious from the video?

For those who’ve used Cassie robots, the power-up and power-down ergonomics are a lot more user friendly. Digit can be disassembled into carry-on luggage sized pieces (give or take) in under 5 minutes for easy transport. The battery charges in-situ using a normal laptop-style charger.

I’m curious about this “stompy” sort of gait that we see in Digit and many other bipedal robots—are there significant challenges or drawbacks to implementing a more human-like (and presumably quieter) heel-toe gait?

There are no drawbacks other than increased complexity in controls and foot design. With Digit v2, the larger surface area helps with the noise, and v2 has similar or better passive-dynamic performance as compared to Cassie or Digit v1. The foot design is brand new, and new behaviors like heel-toe are an active area of development.

How close is Digit v2 to a system that you’d be comfortable operating commercially?

We’re on track for a 2020 launch for Digit v3. Changes from v2 to v3 are mostly bug-fix in nature, with a few regulatory upgrades like full battery certification. Safety is a major concern for us, and we have launch customers that will be operating Digit in a safe environment, with a phased approach to relaxing operational constraints. Digit operates almost exclusively under force control (as with cobots more generally), but at the moment we’ll err on the side of caution during operation until we have the stats to back up safety and reliability. The legged robot industry has too much potential for us to screw it up by behaving irresponsibly.

It will be a while before Digit (or any other humanoid robot) is operating fully autonomously in crowds of people, but there are so many large market opportunities (think indoor factory/warehouse environments) to address prior to that point that we expect to mature the operational safety side of things well in advance of having saturated the more robot-tolerant markets.

[ Agility Robotics ] Continue reading →

Posted in Human Robots

#436065 From Mainframes to PCs: What Robot ...

Posted on October 8, 2019 by Android

This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE.

Autonomous robots are coming around slowly. We already got autonomous vacuum cleaners, autonomous lawn mowers, toys that bleep and blink, and (maybe) soon autonomous cars. Yet, generation after generation, we keep waiting for the robots that we all know from movies and TV shows. Instead, businesses seem to get farther and farther away from the robots that are able to do a large variety of tasks using general-purpose, human anatomy-inspired hardware.

Although these are the droids we have been looking for, anything that came close, such as Willow Garage’s PR2 or Rethink Robotics’ Baxter has bitten the dust. With building a robotic company being particularly hard, compounding business risk with technological risk, the trend goes from selling robots to selling actual services like mowing your lawn, provide taxi rides, fulfilling retail orders, or picking strawberries by the pound. Unfortunately for fans of R2-D2 and C-3PO, these kind of business models emphasize specialized, room- or fridge-sized hardware that is optimized for one very specific task, but does not contribute to a general-purpose robotic platform.

We have actually seen something very similar in the personal computer (PC) industry. In the 1950s, even though computers could be as big as an entire room and were only available to a selected few, the public already had a good idea of what computers would look like. A long list of fictional computers started to populate mainstream entertainment during that time. In a 1962 New York Times article titled “Pocket Computer to Replace Shopping List,” visionary scientist John Mauchly stated that “there is no reason to suppose the average boy or girl cannot be master of a personal computer.”

In 1968, Douglas Engelbart gave us the “mother of all demos,” browsing hypertext on a graphical screen and a mouse, and other ideas that have become standard only decades later. Now that we have finally seen all of this, it might be helpful to examine what actually enabled the computing revolution to learn where robotics is really at and what we need to do next.

The parallels between computers and robots

In the 1970s, mainframes were about to be replaced by the emerging class of mini-computers, fridge-sized devices that cost less than US $25,000 ($165,000 in 2019 dollars). These computers did not use punch-cards, but could be programmed in Fortran and BASIC, dramatically expanding the ease with which potential applications could be created. Yet it was still unclear whether mini-computers could ever replace big mainframes in applications that require fast and efficient processing of large amounts of data, let alone enter every living room. This is very similar to the robotics industry right now, where large-scale factory robots (mainframes) that have existed since the 1960s are seeing competition from a growing industry of collaborative robots that can safely work next to humans and can easily be installed and programmed (minicomputers). As in the ’70s, applications for these devices that reach system prices comparable to that of a luxury car are quite limited, and it is hard to see how they could ever become a consumer product.

Yet, as in the computer industry, successful architectures are quickly being cloned, driving prices down, and entirely new approaches on how to construct or program robotic arms are sprouting left and right. Arm makers are joined by manufacturers of autonomous carts, robotic grippers, and sensors. These components can be combined, paving the way for standard general purpose platforms that follow the model of the IBM PC, which built a capable, open architecture relying as much on commodity parts as possible.

General purpose robotic systems have not been successful for similar reasons that general purpose, also known as “personal,” computers took decades to emerge. Mainframes were custom-built for each application, while typewriters got smarter and smarter, not really leaving room for general purpose computers in between. Indeed, given the cost of hardware and the relatively little abilities of today’s autonomous robots, it is almost always smarter to build a special purpose machine than trying to make a collaborative mobile manipulator smart.

A current example is e-commerce grocery fulfillment. The current trend is to reserve underutilized parts of a brick-and-mortar store for a micro-fulfillment center that stores goods in little crates with an automated retrieval system and a (human) picker. A number of startups like Alert Innovation, Fabric, Ocado Technology, TakeOff Technologies, and Tompkins Robotics, to just name a few, have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves. Such a robotic store clerk would come much closer to our vision of a general purpose robot, but would require many copies of itself that crowd the aisles to churn out hundreds of orders per hour as a microwarehouse could. Although eventually more efficient, the margins in retail are already low and make it unlikely that this industry will produce the technological jump that we need to get friendly C-3POs manning the aisles.

Startups have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves, and would come much closer to our vision of a general purpose robot.

Mainframes were also attacked from the bottom. Fascination with the new digital technology has led to a hobbyist movement to create microcomputers that were sold via mail order or at RadioShack. Initially, a large number of small businesses was selling tens, at most hundreds, of devices, usually as a kit and with wooden enclosures. This trend culminated into the “1977 Trinity” in the form of the Apple II, the Commodore PET, and the Tandy TRS-80, complete computers that were sold for prices around $2500 (TRS) to $5000 (Apple) in today’s dollars. The main application of these computers was their programmability (in BASIC), which would enable consumers to “learn to chart your biorhythms, balance your checking account, or even control your home environment,” according to an original Apple advertisement. Similarly, there exists a myriad of gadgets that explore different aspects of robotics such as mobility, manipulation, and entertainment.

As in the fledgling personal computing industry, the advertised functionality was at best a model of the real deal. A now-famous milestone in entertainment robotics was the original Sony’s Aibo, a robotic dog that was advertised to have many properties that a real dog has such as develop its own personality, play with a toy, and interact with its owner. Released in 1999, and re-launched in 2018, the platform has a solid following among hobbyists and academics who like its programmability, but probably only very few users who accept the device as a pet stand-in.

There also exist countless “build-your-own-robotic-arm” kits. One of the more successful examples is the uArm, which sells for around $800, and is advertised to perform pick and place, assembly, 3D printing, laser engraving, and many other things that sound like high value applications. Using compelling videos of the robot actually doing these things in a constrained environment has led to two successful crowd-funding campaigns, and have established the robot as a successful educational tool.

Finally, there exist platforms that allow hobbyist programmers to explore mobility to construct robots that patrol your house, deliver items, or provide their users with telepresence abilities. An example of that is the Misty II. Much like with the original Apple II, there remains a disconnect between the price of the hardware and the fidelity of the applications that were available.

For computers, this disconnect began to disappear with the invention of the first electronic spreadsheet software VisiCalc that spun out of Harvard in 1979 and prompted many people to buy an entire microcomputer just to run the program. VisiCalc was soon joined by WordStar, a word processing application, that sold for close to $2000 in today’s dollars. WordStar, too, would entice many people to buy the entire hardware just to use the software. The two programs are early examples of what became known as “killer application.”

With factory automation being mature, and robots with the price tag of a minicomputer being capable of driving around and autonomously carrying out many manipulation tasks, the robotics industry is somewhere where the PC industry was between 1973—the release of the Xerox Alto, the first computer with a graphical user interface, mouse, and special software—and 1979—when microcomputers in the under $5000 category began to take off.

Killer apps for robots
So what would it take for robotics to continue to advance like computers did? The market itself already has done a good job distilling what the possible killer apps are. VCs and customers alike push companies who have set out with lofty goals to reduce their offering to a simple value proposition. As a result, companies that started at opposite ends often converge to mirror images of each other that offer very similar autonomous carts, (bin) picking, palletizing, depalletizing, or sorting solutions. Each of these companies usually serves a single application to a single vertical—for example bin-picking clothes, transporting warehouse goods, or picking strawberries by the pound. They are trying to prove that their specific technology works without spreading themselves too thin.

Very few of these companies have really taken off. One example is Kiva Systems, which turned into the logistic robotics division of Amazon. Kiva and others are structured around sound value propositions that are grounded in well-known user needs. As these solutions are very specialized, however, it is unlikely that they result into any economies of scale of the same magnitude that early computer users who bought both a spreadsheet and a word processor application for their expensive minicomputer could enjoy. What would make these robotic solutions more interesting is when functionality becomes stackable. Instead of just being able to do bin picking, palletizing, and transportation with the same hardware, these three skills could be combined to model entire processes.

A skill that is yet little addressed by startups and is historically owned by the mainframe equivalent of robotics is assembly of simple mechatronic devices. The ability to assemble mechatronic parts is equivalent to other tasks such as changing a light bulb, changing the batteries in a remote control, or tending machines like a lever-based espresso machine. These tasks would involve the autonomous execution of complete workflows possible using a single machine, eventually leading to an explosion of industrial productivity across all sectors. For example, picking up an item from a bin, arranging it on the robot, moving it elsewhere, and placing it into a shelf or a machine is a process that equally applies to a manufacturing environment, a retail store, or someone’s kitchen.

Image: Robotic Materials Inc.

Autonomous, vision and force-based assembly of the
Siemens robot learning challenge.

Even though many of the above applications are becoming possible, it is still very hard to get a platform off the ground without added components that provide “killer app” value of their own. Interesting examples are Rethink Robotics or the Robot Operating System (ROS). Rethink Robotics’ Baxter and Sawyer robots pioneered a great user experience (like the 1973 Xerox Alto, really the first PC), but its applications were difficult to extend beyond simple pick-and-place and palletizing and depalletizing items.

ROS pioneered interprocess communication software that was adapted to robotic needs (multiple computers, different programming languages) and the idea of software modularity in robotics, but—in the absence of a common hardware platform—hasn’t yet delivered a single application, e.g. for navigation, path planning, or grasping, that performs beyond research-grade demonstration level and won’t get discarded once developers turn to production systems. At the same time, an increasing number of robotic devices, such as robot arms or 3D perception systems that offer intelligent functionality, provide other ways to wire them together that do not require an intermediary computer, while keeping close control over the real-time aspects of their hardware.

Image: Robotic Materials Inc.

Robotic Materials GPR-1 combines a MIR-100 autonomous cart with an UR-5 collaborative robotic arm, an onRobot force/torque sensor and Robotic Materials’ SmartHand to perform out-of-the-box mobile assembly, bin picking, palletizing, and depalletizing tasks.

At my company, Robotic Materials Inc., we have made strides to identify a few applications such as bin picking and assembly, making them configurable with a single click by combining machine learning and optimization with an intuitive user interface. Here, users can define object classes and how to grasp them using a web browser, which then appear as first-class objects in a robot-specific graphical programming language. We have also done this for assembly, allowing users to stack perception-based picking and force-based assembly primitives by simply dragging and dropping appropriate commands together.

While such an approach might answer the question of a killer app for robots priced in the “minicomputer” range, it is unclear how killer app-type value can be generated with robots in the less-than-$5000 category. A possible answer is two-fold: First, with low-cost arms, mobility platforms, and entertainment devices continuously improving, a confluence of technology readiness and user innovation, like with the Apple II and VisiCalc, will eventually happen. For example, there is not much innovation needed to turn Misty into a home security system; the uArm into a low-cost bin-picking system; or an Aibo-like device into a therapeutic system for the elderly or children with autism.

Second, robots and their components have to become dramatically cheaper. Indeed, computers have seen an exponential reduction in price accompanied by an exponential increase in computational power, thanks in great part to Moore’s Law. This development has helped robotics too, allowing us to reach breakthroughs in mobility and manipulation due to the ability to process massive amounts of image and depth data in real-time, and we can expect it to continue to do so.

Is there a Moore’s Law for robots?
One might ask, however, how a similar dynamics might be possible for robots as a whole, including all their motors and gears, and what a “Moore’s Law” would look like for the robotics industry. Here, it helps to remember that the perpetuation of Moore’s Law is not the reason, but the result of the PC revolution. Indeed, the first killer apps for bookkeeping, editing, and gaming were so good that they unleashed tremendous consumer demand, beating the benchmark on what was thought to be physically possible over and over again. (I vividly remember 56 kbps to be the absolute maximum data rate for copper phone lines until DSL appeared.)

That these economies of scale are also applicable to mechatronics is impressively demonstrated by the car industry. A good example is the 2020 Prius Prime, a highly computerized plug-in hybrid, that is available for one third of the cost of my company’s GPR-1 mobile manipulator while being orders of magnitude more complex, sporting an electrical motor, a combustion engine, and a myriad of sensors and computers. It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal. Given that these robots are part of the equation, actively lowering cost of production, this might happen as fast as never before in the history of industrialization.

It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal.

There is one more driver that might make robots exponentially more capable: the cloud. Once a general purpose robot has learned or was programmed with a new skill, it could share it with every other robot. At some point, a grocer who buys a robot could assume that it already knows how to recognize and handle 99 percent of the retail items in the store. Likewise, a manufacturer can assume that the robot can handle and assemble every item available from McMaster-Carr and Misumi. Finally, families could expect a robot to know every kitchen item that Ikea and Pottery Barn is selling. Sounds like a labor intense problem, but probably more manageable than collecting footage for Google’s Street View using cars, tricycles, and snowmobiles, among other vehicles.

Strategies for robot startups
While we are waiting for these two trends—better and better applications and hardware with decreasing cost—to converge, we as a community have to keep exploring what the canonical robotic applications beyond mobility, bin picking, palletizing, depalletizing, and assembly are. We must also continue to solve the fundamental challenges that stand in the way of making these solutions truly general and robust.

For both questions, it might help to look at the strategies that have been critical in the development of the personal computer, which might equally well apply to robotics:

Start with a solution to a problem your customers have. Unfortunately, their problem is almost never that they need your sensor, widget, or piece of code, but something that already costs them money or negatively affects them in some other way. Example: There are many more people who had a problem calculating their taxes (and wanted to buy VisiCalc) than writing their own solution in BASIC.

Build as little of your own hardware as necessary. Your business model should be stronger than the margin you can make on the hardware. Why taking the risk? Example: Why build your own typewriter if you can write the best typewriting application that makes it worth buying a computer just for that?

If your goal is a platform, make sure it comes with a killer application, which alone justifies the platform cost. Example: Microcomputer companies came and went until the “1977 Trinity” intersected with the killer apps spreadsheet and word processors. Corollary: You can also get lucky.

Use an open architecture, which creates an ecosystem where others compete on creating better components and peripherals, while allowing others to integrate your solution into their vertical and stack it with other devices. Example: Both the Apple II and the IBM PC were completely open architectures, enabling many clones, thereby growing the user and developer base.

It’s worthwhile pursuing this. With most business processes already being digitized, general purpose robots will allow us to fill in gaps in mobility and manipulation, increasing productivity at levels only limited by the amount of resources and energy that are available, possibly creating a utopia in which creativity becomes the ultimate currency. Maybe we’ll even get R2-D2.

Nikolaus Correll is an associate professor of computer science at the University of Colorado at Boulder where he works on mobile manipulation and other robotics applications. He’s co-founder and CTO of Robotic Materials Inc., which is supported by the National Science Foundation and the National Institute of Standards and Technology via their Small Business Innovative Research (SBIR) programs. Continue reading →

Posted in Human Robots

#435806 Boston Dynamics’ Spot Robot Dog ...

Posted on September 24, 2019 by Android

Boston Dynamics is announcing this morning that Spot, its versatile quadruped robot, is now for sale. The machine’s animal-like behavior regularly electrifies crowds at tech conferences, and like other Boston Dynamics’ robots, Spot is a YouTube sensation whose videos amass millions of views.

Now anyone interested in buying a Spot—or a pack of them—can go to the company’s website and submit an order form. But don’t pull out your credit card just yet. Spot may cost as much as a luxury car, and it is not really available to consumers. The initial sale, described as an “early adopter program,” is targeting businesses. Boston Dynamics wants to find customers in select industries and help them deploy Spots in real-world scenarios.

“What we’re doing is the productization of Spot,” Boston Dynamics CEO Marc Raibert tells IEEE Spectrum. “It’s really a milestone for us going from robots that work in the lab to these that are hardened for work out in the field.”

Boston Dynamics has always been a secretive company, but last month, in preparation for launching Spot (formerly SpotMini), it allowed our photographers into its headquarters in Waltham, Mass., for a special shoot. In that session, we captured Spot and also Atlas—the company’s highly dynamic humanoid—in action, walking, climbing, and jumping.

You can see Spot’s photo interactives on our Robots Guide. (The Atlas interactives will appear in coming weeks.)

Gif: Bob O’Connor/Robots.ieee.org

And if you’re in the market for a robot dog, here’s everything we know about Boston Dynamics’ plans for Spot.

Who can buy a Spot?
If you’re interested in one, you should go to Boston Dynamics’ website and take a look at the information the company requires from potential buyers. Again, the focus is on businesses. Boston Dynamics says it wants to get Spots out to initial customers that “either have a compelling use case or a development team that we believe can do something really interesting with the robot,” says VP of business development Michael Perry. “Just because of the scarcity of the robots that we have, we’re going to have to be selective about which partners we start working together with.”

What can Spot do?
As you’ve probably seen on the YouTube videos, Spot can walk, trot, avoid obstacles, climb stairs, and much more. The robot’s hardware is almost completely custom, with powerful compute boards for control, and five sensor modules located on every side of Spot’s body, allowing it to survey the space around itself from any direction. The legs are powered by 12 custom motors with a reduction, with a top speed of 1.6 meters per second. The robot can operate for 90 minutes on a charge. In addition to the basic configuration, you can integrate up to 14 kilograms of extra hardware to a payload interface. Among the payload packages Boston Dynamics plans to offer are a 6 degrees-of-freedom arm, a version of which can be seen in some of the YouTube videos, and a ring of cameras called SpotCam that could be used to create Street View–type images inside buildings.

Image: Boston Dynamics

How do you control Spot?
Learning to drive the robot using its gaming-style controller “takes 15 seconds,” says CEO Marc Raibert. He explains that while teleoperating Spot, you may not realize that the robot is doing a lot of the work. “You don’t really see what that is like until you’re operating the joystick and you go over a box and you don’t have to do anything,” he says. “You’re practically just thinking about what you want to do and the robot takes care of everything.” The control methods have evolved significantly since the company’s first quadruped robots, machines like BigDog and LS3. “The control in those days was much more monolithic, and now we have what we call a sequential composition controller,” Raibert says, “which lets the system have control of the dynamics in a much broader variety of situations.” That means that every time one of Spot’s feet touches or doesn’t touch the ground, this different state of the body affects the basic physical behavior of the robot, and the controller adjusts accordingly. “Our controller is designed to understand what that state is and have different controls depending upon the case,” he says.

How much does Spot cost?
Boston Dynamics would not give us specific details about pricing, saying only that potential customers should contact them for a quote and that there is going to be a leasing option. It’s understandable: As with any expensive and complex product, prices can vary on a case by case basis and depend on factors such as configuration, availability, level of support, and so forth. When we pressed the company for at least an approximate base price, Perry answered: “Our general guidance is that the total cost of the early adopter program lease will be less than the price of a car—but how nice a car will depend on the number of Spots leased and how long the customer will be leasing the robot.”

Can Spot do mapping and SLAM out of the box?
The robot’s perception system includes cameras and 3D sensors (there is no lidar), used to avoid obstacles and sense the terrain so it can climb stairs and walk over rubble. It’s also used to create 3D maps. According to Boston Dynamics, the first software release will offer just teleoperation. But a second release, to be available in the next few weeks, will enable more autonomous behaviors. For example, it will be able to do mapping and autonomous navigation—similar to what the company demonstrated in a video last year, showing how you can drive the robot through an environment, create a 3D point cloud of the environment, and then set waypoints within that map for Spot to go out and execute that mission. For customers that have their own autonomy stack and are interested in using those on Spot, Boston Dynamics made it “as plug and play as possible in terms of how third-party software integrates into Spot’s system,” Perry says. This is done mainly via an API.

How does Spot’s API works?
Boston Dynamics built an API so that customers can create application-level products with Spot without having to deal with low-level control processes. “Rather than going and building joint-level kinematic access to the robot,” Perry explains, “we created a high-level API and SDK that allows people who are used to Web app development or development of missions for drones to use that same scope, and they’ll be able to build applications for Spot.”

What applications should we see first?
Boston Dynamics envisions Spot as a platform: a versatile mobile robot that companies can use to build applications based on their needs. What types of applications? The company says the best way to find out is to put Spot in the hands of as many users as possible and let them develop the applications. Some possibilities include performing remote data collection and light manipulation in construction sites; monitoring sensors and infrastructure at oil and gas sites; and carrying out dangerous missions such as bomb disposal and hazmat inspections. There are also other promising areas such as security, package delivery, and even entertainment. “We have some initial guesses about which markets could benefit most from this technology, and we’ve been engaging with customers doing proof-of-concept trials,” Perry says. “But at the end of the day, that value story is really going to be determined by people going out and exploring and pushing the limits of the robot.”

Photo: Bob O'Connor

How many Spots have been produced?
Last June, Boston Dynamics said it was planning to build about a hundred Spots by the end of the year, eventually ramping up production to a thousand units per year by the middle of this year. The company admits that it is not quite there yet. It has built close to a hundred beta units, which it has used to test and refine the final design. This version is now being mass manufactured, but the company is still “in the early tens of robots,” Perry says.

How did Boston Dynamics test Spot?

The company has tested the robots during proof-of-concept trials with customers, and at least one is already using Spot to survey construction sites. The company has also done reliability tests at its facility in Waltham, Mass. “We drive around, not quite day and night, but hundreds of miles a week, so that we can collect reliability data and find bugs,” Raibert says.

What about competitors?
In recent years, there’s been a proliferation of quadruped robots that will compete in the same space as Spot. The most prominent of these is ANYmal, from ANYbotics, a Swiss company that spun out of ETH Zurich. Other quadrupeds include Vision from Ghost Robotics, used by one of the teams in the DARPA Subterranean Challenge; and Laikago and Aliengo from Unitree Robotics, a Chinese startup. Raibert views the competition as a positive thing. “We’re excited to see all these companies out there helping validate the space,” he says. “I think we’re more in competition with finding the right need [that robots can satisfy] than we are with the other people building the robots at this point.”

Why is Boston Dynamics selling Spot now?
Boston Dynamics has long been an R&D-centric firm, with most of its early funding coming from military programs, but it says commercializing robots has always been a goal. Productizing its machines probably accelerated when the company was acquired by Google’s parent company, Alphabet, which had an ambitious (and now apparently very dead) robotics program. The commercial focus likely continued after Alphabet sold Boston Dynamics to SoftBank, whose famed CEO, Masayoshi Son, is known for his love of robots—and profits.

Which should I buy, Spot or Aibo?
Don’t laugh. We’ve gotten emails from individuals interested in purchasing a Spot for personal use after seeing our stories on the robot. Alas, Spot is not a bigger, fancier Aibo pet robot. It’s an expensive, industrial-grade machine that requires development and maintenance. If you’re maybe Jeff Bezos you could probably convince Boston Dynamics to sell you one, but otherwise the company will prioritize businesses.

What’s next for Boston Dynamics?
On the commercial side of things, other than Spot, Boston Dynamics is interested in the logistics space. Earlier this year it announced the acquisition of Kinema Systems, a startup that had developed vision sensors and deep-learning software to enable industrial robot arms to locate and move boxes. There’s also Handle, the mobile robot on whegs (wheels + legs), that can pick up and move packages. Boston Dynamics is hiring both in Waltham, Mass., and Mountain View, Calif., where Kinema was located.

Okay, can I watch a cool video now?
During our visit to Boston Dynamics’ headquarters last month, we saw Atlas and Spot performing some cool new tricks that we unfortunately are not allowed to tell you about. We hope that, although the company is putting a lot of energy and resources into its commercial programs, Boston Dynamics will still find plenty of time to improve its robots, build new ones, and of course, keep making videos. [Update: The company has just released a new Spot video, which we’ve embedded at the top of the post.][Update 2: We should have known. Boston Dynamics sure knows how to create buzz for itself: It has just released a second video, this time of Atlas doing some of those tricks we saw during our visit and couldn’t tell you about. Enjoy!]

[ Boston Dynamics ] Continue reading →

Posted in Human Robots

#435791 To Fly Solo, Racing Drones Have a Need ...

Posted on September 21, 2019 by Android

Drone racing’s ultimate vision of quadcopters weaving nimbly through obstacle courses has attracted far less excitement and investment than self-driving cars aimed at reshaping ground transportation. But the U.S. military and defense industry are betting on autonomous drone racing as the next frontier for developing AI so that it can handle high-speed navigation within tight spaces without human intervention.

The autonomous drone challenge requires split-second decision-making with six degrees of freedom instead of a car’s mere two degrees of road freedom. One research team developing the AI necessary for controlling autonomous racing drones is the Robotics and Perception Group at the University of Zurich in Switzerland. In late May, the Swiss researchers were among nine teams revealed to be competing in the two-year AlphaPilot open innovation challenge sponsored by U.S. aerospace company Lockheed Martin. The winning team will walk away with up to $2.25 million for beating other autonomous racing drones and a professional human drone pilot in head-to-head competitions.

“I think it is important to first point out that having an autonomous drone to finish a racing track at high speeds or even beating a human pilot does not imply that we can have autonomous drones [capable of] navigating in real-world, complex, unstructured, unknown environments such as disaster zones, collapsed buildings, caves, tunnels or narrow pipes, forests, military scenarios, and so on,” says Davide Scaramuzza, a professor of robotics and perception at the University of Zurich and ETH Zurich. “However, the robust and computationally efficient state estimation algorithms, control, and planning algorithms developed for autonomous drone racing would represent a starting point.”

The nine teams that made the cut—from a pool of 424 AlphaPilot applicants—will compete in four 2019 racing events organized under the Drone Racing League’s Artificial Intelligence Robotic Racing Circuit, says Keith Lynn, program manager for AlphaPilot at Lockheed Martin. To ensure an apples-to-apples comparison of each team’s AI secret sauce, each AlphaPilot team will upload its AI code into identical, specially-built drones that have the NVIDIA Xavier GPU at the core of the onboard computing hardware.

“Lockheed Martin is offering mentorship to the nine AlphaPilot teams to support their AI tech development and innovations,” says Lynn. The company “will be hosting a week-long Developers Summit at MIT in July, dedicated to workshopping and improving AlphaPilot teams’ code,” he added. He notes that each team will retain the intellectual property rights to its AI code.

The AlphaPilot challenge takes inspiration from older autonomous drone racing events hosted by academic researchers, Scaramuzza says. He credits Hyungpil Moon, a professor of robotics and mechanical engineering at Sungkyunkwan University in South Korea, for having organized the annual autonomous drone racing competition at the International Conference on Intelligent Robots and Systems since 2016.

It’s no easy task to create and train AI that can perform high-speed flight through complex environments by relying on visual navigation. One big challenge comes from how drones can accelerate sharply, take sharp turns, fly sideways, do zig-zag patterns and even perform back flips. That means camera images can suddenly appear tilted or even upside down during drone flight. Motion blur may occur when a drone flies very close to structures at high speeds and camera pixels collect light from multiple directions. Both cameras and visual software can also struggle to compensate for sudden changes between light and dark parts of an environment.

To lend AI a helping hand, Scaramuzza’s group recently published a drone racing dataset that includes realistic training data taken from a drone flown by a professional pilot in both indoor and outdoor spaces. The data, which includes complicated aerial maneuvers such as back flips, flight sequences that cover hundreds of meters, and flight speeds of up to 83 kilometers per hour, was presented at the 2019 IEEE International Conference on Robotics and Automation.

The drone racing dataset also includes data captured by the group’s special bioinspired event cameras that can detect changes in motion on a per-pixel basis within microseconds. By comparison, ordinary cameras need milliseconds (each millisecond being 1,000 microseconds) to compare motion changes in each image frame. The event cameras have already proven capable of helping drones nimbly dodge soccer balls thrown at them by the Swiss lab’s researchers.

The Swiss group’s work on the racing drone dataset received funding in part from the U.S. Defense Advanced Research Projects Agency (DARPA), which acts as the U.S. military’s special R&D arm for more futuristic projects. Specifically, the funding came from DARPA’s Fast Lightweight Autonomy program that envisions small autonomous drones capable of flying at high speeds through cluttered environments without GPS guidance or communication with human pilots.

Such speedy drones could serve as military scouts checking out dangerous buildings or alleys. They could also someday help search-and-rescue teams find people trapped in semi-collapsed buildings or lost in the woods. Being able to fly at high speed without crashing into things also makes a drone more efficient at all sorts of tasks by making the most of limited battery life, Scaramuzza says. After all, most drone battery life gets used up by the need to hover in flight and doesn’t get drained much by flying faster.

Even if AI manages to conquer the drone racing obstacle courses, that would be the end of the beginning of the technology’s development. What would still be required? Scaramuzza specifically singled out the need to handle low-visibility conditions involving smoke, dust, fog, rain, snow, fire, hail, as some of the biggest challenges for vision-based algorithms and AI in complex real-life environments.

“I think we should develop and release datasets containing smoke, dust, fog, rain, fire, etc. if we want to allow using autonomous robots to complement human rescuers in saving people lives after an earthquake or natural disaster in the future,” Scaramuzza says. Continue reading →

Posted in Human Robots