Monthly Archives: October 2019

The SAM100 bricklaying robot at the Brighton Health Center South site of the University of Michigan Hospitals and Health Centers. Source: Construction Robotics

Until recently, construction was one of the least digitized and automated industries in the world. Many projects could be completed more efficiently with the help of the right construction robotics, mainly because the related tasks are incredibly repetitive.

While manual labor will likely always be a huge component of modern construction, technology has been steadily improving since the first pulleys and power tools. Robots, drones, autonomous vehicles, 3D printing, and exoskeletons are beginning to help get the work done. With low U.S. unemployment and shortages of skilled labor, automation is key to meeting demand and continued economic growth.

Construction robots may be involved in specific tasks, such as bricklaying, painting, loading, and bulldozing. We expect hundreds of AMRs in the next two years, mainly doing haulage, said Rian Whitton, an analyst at ABI Research. These robots help to protect workers from a hazardous working environment, reduce workplace injuries, and address labor shortages.

Many potential solutions rely on artificial intelligence and machine learning to deliver unprecedented levels of data-driven support. For instance, a driverless crane could transport materials around a worksite, or an aerial drone could gather information on a worksite to be compared against the plan.

Here are just a few examples of how robotics is transforming construction.

An example of how construction robotics are revolutionizing the industry can be seen in the HadrianX bricklaying machine from Australia-based FBR Ltd. (also known as Fastbrick Robotics). It employs an intelligent control system aided by CAD to calculate the necessary materials and movements for bricklaying.

Hadrian also measures environmental changes, such as movement caused by wind or vibrations, in real time. This data is then used to improve precision during the building process.

While Hadrian does require the use of proprietary blocks and adhesive, FBR noted that the related materials are 12 times bigger than standard house bricks and are lighter, stronger, and more environmentally sustainable.

Robots like Hadrian and SAM100 from Victor, N.Y.-based Construction Robotics promise to reduce operating costs and waste, as well as provide safer work environments and improve productivity. Hadrian can build the walls of a house in a single day, which is much faster than conventional methods.

While the major automakers and technology companies are working on self-driving cars, autonomous vehicles are already part of construction robotics.

Such equipment can transport supplies and materials. For instance, Volvo has been working on its HX2, an autonomous and electric load carrier that can move heavy loads without additional input. It has no driver cab and instead uses a digital logistics-driven control technology backed by what Volvo calls a vision system to detect humans and obstacles while on the move.

Another company, Built Robotics, which last month raised $33 million, offers autonomous bulldozers and excavators. AI guidance systems direct the equipment to their destinations and ensure that the necessary work is completed safely and accurately.

Autonomous vehicles and construction robotics is not intended to replace manual labor entirely, but to augment and enhance efficiency. Safety is vastly improved as well, as we eliminate the potential for human error.

Construction robotics and drones using sensors such as lidar with Global Positioning System technologies can provide vital information about a worksite. Along with AI, it can help predict what tasks are required.

Doxel Inc. makes a small tread-based robot that does exactly that. It scans and assesses the progress of a construction project by traversing the site. The information it collects is used to detect potential errors and problems early.

Doxels data is stored in the cloud, where its filtered through a deep-learning algorithm to recognize and assess more minute details. For example, the system might point out that a ventilation duct is installed incorrectly, and the early detection can allow for the proper correction well before costly revisions are needed.

Humans are still in the loop for much of construction robotics, combining the strengths of human supervision with multiple technologies. The Internet of Things, additive manufacturing, and digitization are contributing to the industrys growth, noted Caterpillar.

Painting drones are an excellent example, since they can be controlled via tablet or smartphone via an app, and they can report on the data they gather thats analyzed in the cloud.

Remote-control technology can also be applied to semi-autonomous vehicles. Project managers can use it to deliver instructions and orders to their workforce instantly.

Barcelona-based Scaled Robotics offers construction robotics that can be remotely controlled by mobile devices. The companys Husky unmanned ground vehicle can roam a construction site and capture critical information via multiple sensors. The data is transferred to the cloud, where its used for building information modeling (BIM) of the project.

Before, during, and after a construction project, many assessments require the review of a worksite and surrounding area. Limited surveillance is also necessary for supervising workers and securing the site. In addition, project managers and supervisors must walk the site to conduct final inspections. Construction robotics and drones can help all of these processes.

Aerial drones and ground-based robots can survey a worksite and gather multiple types of data, depending on the sensors used. Augmented reality and virtual reality can enable operators to get a realistic and real-time feel for what the drones are seeing.

While donning a VR headset, for instance, viewers can see a live feed of captured video from the drone. More importantly, that immersive experience is provided remotely, so project managers dont even have to be on the job site to get an accurate assessment. The video feed is also recorded for playback at a later time, providing yet another resource.

Companies are already using drone technology to this end. In 2018, Chinese drone maker DJI announced a global partnership with Skycatch for a fleet of 1,000 high-precision custom drones to create 3D site maps and models of project sites.

The global market for construction robotics also represents a huge opportunity for developers and suppliers. It could grow from $22.7 million in 2018 to $226 million by 2025, predicts Tractica. Research and Markets estimates that the market will grow to $126.4 million by 2025.

According to the International Federation of Robotics and the Robotic Industries Association, the construction robotics market will experience a compound annual growth rate (CAGR) of 8.7% between 2018 and 2022. Research firm IDC is more bullish, predicting a CAGR of 20.2%.

Automation and digitization are driving a revolution in the construction industry, which has historically been slow to adopt new technologies. From design through final inspection and maintenance, the full benefits of construction robotics have yet to be realized.

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Construction robotics is changing the industry in these 5 ways - Robot Report

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Last week, a robotic hand successfully solved a Rubiks Cube. While that feat might seem like a fun parlor trick, its a sign that robots are being programmed to learn and not just memorize.

Robots are already playing important roles inside retail giants like Amazon and manufacturing companies like Foxconn by completing very specific, repetitive tasks. But many believe that machine learning will ultimately allow robots to master a much wider array of more complex functions.

When you imagine the future of robots and artificial intelligence, do you get excited? Do you envision a world of benefits for humankind? Or does an automated future fill you with concern and fear?

In If a Robotic Hand Solves a Rubiks Cube, Does It Prove Something? Cade Metz writes about how this five-fingered feat could show important progress in A.I. research:

Last week, on the third floor of a small building in San Franciscos Mission District, a woman scrambled the tiles of a Rubiks Cube and placed it in the palm of a robotic hand.

The hand began to move, gingerly spinning the tiles with its thumb and four long fingers. Each movement was small, slow and unsteady. But soon, the colors started to align. Four minutes later, with one more twist, it unscrambled the last few tiles, and a cheer went up from a long line of researchers watching nearby.

The researchers worked for a prominent artificial intelligence lab, OpenAI, and they had spent several months training their robotic hand for this task.

Though it could be dismissed as an attention-grabbing stunt, the feat was another step forward for robotics research. Many researchers believe it was an indication that they could train machines to perform far more complex tasks. That could lead to robots that can reliably sort through packages in a warehouse or to cars that can make decisions on their own.

The article continues:

A robot that can solve a Rubiks Cube is not new. Researchers previously designed machines specifically for the task devices that look nothing like a hand and they can solve the puzzle in less than a second. But building devices that work like a human hand is a painstaking process in which engineers spend months laying down rules that define each tiny movement.

The OpenAI project was an achievement of sorts because its researchers did not program each movement into their robotic hand. That might take decades, if not centuries, considering the complexity of a mechanical device with a thumb and four fingers. The labs researchers built a computer system that learned to solve the Rubiks Cube largely on its own.

What is exciting about this work is that the system learns, said Jeff Clune, a robotics professor at the University of Wyoming. It doesnt memorize one way to solve the problem. It learns.

Students, read the entire article, then tell us:

How do you feel about the future of robots, artificial intelligence and automation? Do you envision a world of progress? Or dystopian peril?

Do you fear countless professions becoming obsolete, the merging of humans and machines, an age of artificial intimacy or a potential robot uprising? Or are you excited about all the possibilities such a future could hold? Explain why you feel the way you do.

Researchers have already built machines to solve a Rubiks Cube in under a second. And even though OpenAIs hand can solve the puzzle in as little as four minutes, it drops the cube eight times out of 10. In your opinion, how big a deal is this robotic hand demonstration? Why do you think it made the news?

Students 13 and older are invited to comment. All comments are moderated by the Learning Network staff, but please keep in mind that once your comment is accepted, it will be made public.

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Does the Future of Robots Get You Excited, or Fill You With Dread? - The New York Times

If youre at a desk with a pen or pencil handy, try this move: Grab the pen by one end with your thumb and index finger, and push the other end against the desk. Slide your fingers down the pen, then flip it upside down, without letting it drop. Not too hard, right?

But for a robot say, one thats sorting through a bin of objects and attempting to get a good grasp on one of them this is a computationally taxing maneuver. Before even attempting the move it must calculate a litany of properties and probabilities, such as the friction and geometry of the table, the pen, and its two fingers, and how various combinations of these properties interact mechanically, based on fundamental laws of physics.

Now MIT engineers have found a way to significantly speed up the planning process required for a robot to adjust its grasp on an object by pushing that object against a stationary surface. Whereas traditional algorithms would require tens of minutes for planning out a sequence of motions, the new teams approach shaves this preplanning process down to less than a second.

Alberto Rodriguez, associate professor of mechanical engineering at MIT, says the speedier planning process will enable robots, particularly in industrial settings, to quickly figure out how to push against, slide along, or otherwise use features in their environments to reposition objects in their grasp. Such nimble manipulation is useful for any tasks that involve picking and sorting, and even intricate tool use.

This is a way to extend the dexterity of even simple robotic grippers, because at the end of the day, the environment is something every robot has around it, Rodriguez says.

The teams results are published today in The International Journal of Robotics Research. Rodriguez co-authors are lead author Nikhil Chavan-Dafle, a graduate student in mechanical engineering, and Rachel Holladay, a graduate student in electrical engineering and computer science.

Physics in a cone

Rodriguez group works on enabling robots to leverage their environment to help them accomplish physical tasks, such as picking and sorting objects in a bin.

Existing algorithms typically take hours to preplan a sequence of motions for a robotic gripper, mainly because, for every motion that it considers, the algorithm must first calculate whether that motion would satisfy a number of physical laws, such as Newtons laws of motion and Coulombs law describing frictional forces between objects.

Its a tedious computational process to integrate all those laws, to consider all possible motions the robot can do, and to choose a useful one among those, Rodriguez says.

He and his colleagues found a compact way to solve the physics of these manipulations, in advance of deciding how the robots hand should move. They did so by using motion cones, which are essentially visual, cone-shaped maps of friction.

The inside of the cone depicts all the pushing motions that could be applied to an object in a specific location, while satisfying the fundamental laws of physics and enabling the robot to keep hold of the object. The space outside of the cone represents all the pushes that would in some way cause an object to slip out of the robots grasp.

Seemingly simple variations, such as how hard robot grasps the object, can significantly change how the object moves in the grasp when pushed, Holladay explains. Based on how hard youre grasping, there will be a different motion. And thats part of the physical reasoning that the algorithm handles.

The teams algorithm calculates a motion cone for different possible configurations between a robotic gripper, an object that it is holding, and the environment against which it is pushing, in order to select and sequence different feasible pushes to reposition the object.

A new algorithm speeds up the planning process for robotic grippers.A robot in the lab is shown picking up a block letter, T, and pushing it against a nearby wall to re-angle it, before setting it back down in an upright position.

Its a complicated process but still much faster than the traditional method fast enough that planning an entire series of pushes takes half a second, Holladay says.

Big plans

The researchers tested the new algorithm on a physical setup with a three-way interaction, in which a simple robotic gripper was holding a T-shaped block and pushing against a vertical bar. They used multiple starting configurations, with the robot gripping the block at a particular position and pushing it against the bar from a certain angle. For each starting configuration, the algorithm instantly generated the map of all the possible forces that the robot could apply and the position of the block that would result.

We did several thousand pushes to verify our model correctly predicts what happens in the real world, Holladay says. If we apply a push thats inside the cone, the grasped object should remain under control. If its outside, the object should slip from the grasp.

The researchers found that the algorithms predictions reliably matched the physical outcome in the lab, planning out sequences of motions such as reorienting the block against the bar before setting it down on a table in an upright position in less than a second, compared with traditional algorithms that take over 500 seconds to plan out.

Because we have this compact representation of the mechanics of this three-way-interaction between robot, object, and their environment, we can now attack bigger planning problems, Rodriguez says.

The group is hoping to apply and extend its approach to enable a robotic gripper to handle different types of tools, for instance in a manufacturing setting.

Most factory robots that use tools have a specially designed hand, so instead of having the abiity to grasp a screwdriver and use it in a lot of different ways, they just make the hand a screwdriver, Holladay says. You can imagine that requires less dexterous planning, but its much more limiting. Wed like a robot to be able to use and pick lots of different things up.

This research was supported, in part, by Mathworks, the MIT-HKUST Alliance, and the National Science Foundation.

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Giving robots a faster grasp - The MIT Tech

There is much anticipation these days around the field of robotics with its immense potential and promising future applications. However, a large gap exists between public expectations and what is actually deemed technically feasible by scientists and engineers today. Fortunately, Samsungs New York AI Center is buoyed by the presence of a team of highly skilled researchers, led by robotics and AI expert Dr. Daniel D. Lee, who are working to close this gap. Samsung Newsroom spoke with Dr. Lee about the work being done at the center, as well as the facilitys ability to foster collaboration in a range of areas and attract top talent.

Asked about his centers mandate, Lee explains that the New York AI Center focuses on fundamental research at the intersection of AI, robotics and neuroscience. The centers objective is to solve challenging problems at this intersection, and one good example is the problem of robotic manipulation1.

Put simply, robots need to become far more skillful before they are ready to help humans with physical tasks in their daily lives. The first step involves endowing robots with the intelligence to perceive and understand their surroundings. Next, they must be able to make swift decisions in unpredictable situations. Finally, robots should be dexterous and nimble enough to perform the appropriate actions. However, it is impossible for robot designers to anticipate every contingency robots will encounter in real world environments. Thus, robots need to be able to learn from experience just as humans do.

At this time, most common machine learning methods are not suitable for teaching robots since enormous amounts of training data are required. Lee explained that there are several challenges that need to be addressed regarding machine learning for robotics.

Dealing with the physical world is much more difficult for AI than playing video games or Go, he explains, We are currently developing AI learning methods that can deal with the uncertainty and diversity of the physical world so that robots become more prevalent in homes and workplaces. I would compare the state of robots today to computers in the 1980s during the transformation from mainframes to personal computers.

The New York AI Center is addressing such challenges to provide a richer AI and robotics experience. For instance, the center has recently developed novel AI methods that are able to efficiently teach robots using limited data. One recently-developed method trains a neural network to generate motion trajectories for a robot arm directly from camera images.

In order to allow robots to handle things for people, robots need to learn how to touch, grasp, and move a variety of everyday objects. Lee explains how the problem of dexterous robotic manipulation is an area of focus for the New York AI Center.

Lee comments that the ability of humans and some animals to manipulate household objects is currently unmatched by machines. Thats why we are investigating how AI-based solutions can be applied to make breakthroughs in this area. Extrapolating further, Lee explains that dexterous robotic manipulation requires the ability to precisely and robustly handle objects exhibiting uncertain material properties.

Manipulation is relatively easy if the objects and environments are carefully controlled, such as on a factory floor, Lee reports, But it becomes much more difficult in unknown, cluttered environments when faced with a diverse array of objects.

By way of an example, Lee lays out the capabilities that would be required for a robot to serve a chilled glass of wine in a restaurant. How heavy is the glass, and how slippery is it due to condensation? He adds, Its impossible to completely model all the possible physical characteristics of the glass of wine, so machine learning is critical in training robots to handle the difficult situations.

As the AI sector has grown more sophisticated, it has become increasingly clear that collaborative solutions are critical for researchers to overcome the challenges they face. In an area as complex and multi-faceted as robotic manipulation, contributions from and collaborations with the worlds best and brightest will be instrumental, comments Lee. He highlights the value of working with both other Samsung AI Centers and academic institutions, saying that, solving fundamental problems in AI to positively impact society requires drawing upon the ability and skills of numerous experts globally.

He added, The Samsung AI Centers invite collaborations with researchers who can help address these difficult challenges. We currently have a number of faculty from leading academic institutions who are collaborating with us in New York.

Lee highlights just how beneficial being located in New York has been for his team, saying that certainly, New York City is one of the greatest and most diverse cities in the world. It is a magnet for world-class research and engineering talent.

Attracting the very best in talent is extremely important to remain on the bleeding edge of future AI advancements, and Lee reports that the center has been fortunate in this area, saying, We have benefited from being able to attract and recruit some outstanding researchers since we started the Center.

Our team is composed of expert scientists and engineers who are creating innovative theories and algorithms and state-of-the-art technological developments, Lee adds, Its been great working with them to publish in leading academic conferences and journals as well.

Speaking about how he envisions robots will fit into society in the future, Lee points out that, in their infancy, some robots drew attention because they were cute and fun, but that people tended to use them less as the novelty wore off. In order for people to see robots as valuable and relevant, new systems need to have enough intelligence that they become indispensable in our daily lives.

Intelligent robotic systems have the potential to completely revolutionize how people go about their activities in the future, Lee extrapolates, In the near term, we will see modest improvements on simple tasks in constrained environments. But more complete systems that can handle a variety of chores and complex tasks will require further research breakthroughs. The Samsung AI Centers are helping to generate those new advances.

Asked to outline what he sees as the ultimate vision for AI and robotic intelligence, Lee says, I grew up reading and watching science fiction stories that envisaged amazing robots helping humans. It would be incredible to see some of those positive visions actually come to life.

1 The ability for robots to interact with and move physical objects in a range of environments.

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[Hearing from an AI Expert 5] At the Intersection of Robotics and Innovation - Samsung Newsroom