Tag Archives: technology

#440643 Automating Road Maintenance With LiDAR ...

This is a sponsored article brought to you by SICK Inc..
From advanced manufacturing to automated vehicles, engineers are using LiDAR to change the world as we know it. For the second year, students from across the country submitted projects to SICK's annual TiM$10K Challenge.

The first place team during the 2020 TiM$10K Challenge hails from Worcester Polytechnic Institute (WPI) in Worcester, Mass. The team comprised of undergraduate seniors, Daniel Pelaez and Noah Budris, and undergraduate junior, Noah Parker.
With the help of their academic advisor, Dr. Alexander Wyglinski, Professor of Electrical Engineering and Robotics Engineering at WPI, the team took first place in the 2020 TiM$10K Challenge with their project titled ROADGNAR, a mobile and autonomous pavement quality data collection system.
So what is the TiM$10K Challenge? In this challenge, SICK reached out to universities across the nation that were looking to support innovation and student achievement in automation and technology. Participating teams were supplied with a SICK 270° LiDAR, a TiM, and accessories. They were challenged to solve a problem, create a solution, and bring a new application that utilizes the SICK scanner in any industry.
Around the United States, many of the nation's roadways are in poor condition, most often from potholes and cracks in the pavement, which can make driving difficult. Many local governments agree that infrastructure is in need of repair, but with a lack of high-quality data, inconsistencies in damage reporting, and an overall lack of adequate prioritization, this is a difficult problem to solve.

Pelaez, Parker, and Budris first came up with the idea of ROADGNAR before they had even learned of the TiM$10K Challenge. They noticed that the roads in their New England area were in poor condition, and wanted to see if there was a way to help solve the way road maintenance is performed.
In their research, they learned that many local governments use outdated and manual processes. Many send out workers to check for poor road conditions, who then log the information in notebooks.
The team began working on a solution to help solve this problem. It was at a career fair that Pelaez met a SICK representative, who encouraged him to apply to the TiM$10K Challenge.
Win $10K and a Trip to Germany!
SICK is excited to announce the 2022-2023 edition of the SICK TiM$10K Challenge. Twenty teams will be selected to participate in the challenge, and the chosen teams will be supplied with a 270º SICK LiDAR sensor (TiM) and accessories. The teams will be challenged to solve a problem, create a solution, bring a new application that utilizes the SICK LiDAR in any industry. This can be part of the curriculum of a senior design project or capstone projects for students.
The 3 winning teams will win a cash award of
• 1st Place – $10K
• 2nd Place – $5K
• 3rd place – $3K
In addition to bragging rights and the cash prize, the 1st place winning team, along with the advising professor, will be offered an all-expenses-paid trip to SICK Germany to visit the SICK headquarters and manufacturing facility!
Registration is now open for the academic year 2022-2023!

Using SICK's LiDAR technology, the ROADGNAR takes a 3D scan of the road and the data is then used to determine the exact level of repair needed.

ROADGNAR collects detailed data on the surface of any roadway, while still allowing for easy integration onto any vehicle. With this automated system, road maintenance can become a faster, more reliable, and more efficient process for towns and cities around the country.
ROADGNAR solves this problem through two avenues: hardware and software. The team designed two mounting brackets to connect the system to a vehicle. The first, located in the back of the vehicle, supports a LiDAR scanner. The second is fixed in line with the vehicle's axle and supports a wheel encoder, which is wired to the fuse box.
“It definitely took us a while to figure out a way to power ROADGNAR so we wouldn't have to worry about it shutting off while the car was in motion,” said Parker.
Also wired to the fuse box is a GPS module within the vehicle itself. Data transfer wires are attached to these three systems and connected to a central processing unit within the vehicle.
Using LiDAR to collect road dataWhen the car is started, all connected devices turn on. The LiDAR scanner collects road surface data, the wheel encoder tracks an accurate measurement of the distance travelled by the vehicle, and the GPS generates geo-tags on a constant basis. All this data is stored in the onboard database, where a monitor presents it all to the user. The data is then stored in a hard drive.
Much like the roads in their Massachusetts town, the creation process of ROADGNAR was not without its challenges. The biggest problem took the form of the COVID-19 pandemic, which hit the ROADGNAR team in the middle of development. Once WPI closed to encourage its students and faculty to practice social distancing, the team was without a base of operations.
“When the coronavirus closed our school, we were lucky enough to live pretty close to each other,” said Paleaz. “We took precautions, but were able to come together to test and power through to finish our project.”

Integrating LiDAR into the car was also a challenge. Occasionally, the LiDAR would shut off when the car began moving. The team had to take several measures to keep the sensor online, often contacting SICK's help center for instruction.
“One of the major challenges was making sure we were getting enough data on a given road surface,” said Budris. “At first we were worried that we wouldn't get enough data from the sensor to make ROADGNAR feasible, but we figured that if we drove at a slow and constant rate, we'd be able to get accurate scans.”
With the challenge complete, Pelaez, Budris, and Parker are looking to turn ROADGNAR into a genuine product. They have already contacted an experienced business partner to help them determine their next steps.

They are now interviewing with representatives from various Department of Public Works throughout Massachusetts and Connecticut. Thirteen municipalities have indicated that they would be extremely interested in utilizing ROADGNAR, as it would drastically reduce the time needed to assess all the roads in the area. The trio is excited to see how different LiDAR sensors can help refine ROADGNAR into a viable product.
“We'd like to keep the connection going,” explained Pelaez. “If we can keep the door open for a potential partnership between us and SICK, that'd be great.”
SICK is now accepting entries for the TiM$10K Challenge for the 2022-2023 school year!
Student teams are encouraged to use their creativity and technical knowledge to incorporate the SICK LiDAR for any industry in any application. Advisors/professors are allowed to guide the student teams as required. Continue reading

Posted in Human Robots

#440514 It’s Not Too Late to Replace Toxic ...

A year and a half ago Netflix released The Social Dilemma, a docu-drama that dug into the harmful consequences of social media. Think political polarization, the spread of misinformation, and upticks in anxiety and depression across multiple demographics. Tristan Harris, a former Google design ethicist and co-founder of the Center for Humane Technology, is a central figure in the film. In a session at South By Southwest this week, Harris spoke about the steps we should be taking to get this technology and our relationship with it to a healthy place, or as he put it, the wisdom we need to steer technology and our future.

Harris opened with a quote from biologist Edward O. Wilson, who said, “The real problem of humanity is the following: We have Paleolithic emotions, medieval institutions, and godlike technology. And it is terrifically dangerous, and it is now approaching a point of crisis overall.”

In other words, technology is advancing far too fast for our brains to keep up and know how to healthily interact with it, or for our institutions to understand it and wisely regulate it.

Wilson spoke these words at a debate at the Harvard Museum of Natural History in 2009; that is, before the widespread adoption of platforms like Instagram and Tiktok, or of tech like deepfakes, text generators, CRISPR, and other innovations that have the potential to transform humanity (for better or for worse).

Tristan Harris at SXSW 2022
We now have algorithms that can generate realistic images based on text, of anything from mountain sunsets to bombed-out buildings in Ukraine. We have GPT-3, which could write a convincing paper arguing mRNA vaccines aren’t safe, citing real facts that are simply presented out of context. “This is like a neutron bomb for trust on the internet,” Harris said. “And the complexity of the world is increasing every day.” Our ability to respond, however, isn’t matching up.

Issues that would have been considered separate from one another in the past (or that didn’t exist in the past) are now closely linked; consider the impact that misinformation and synthetic media could have on nuclear escalation (and the impact they’ve already had on elections and democracy), or the connection between artificial intelligence and global financial risk.

Our previous thinking around how to manage technology isn’t good enough in the face of this new complexity; how do we handle issues like privacy or freedom of speech when multiple actors are involved, there’s low accountability, and everyone’s definition of what’s “right” is different? “Technology has been undermining humanity’s capacity for wisdom,” Harris said. “Not just individually, but our collective ability to operate with the wisdom that we need.”

Wisdom, he said, means knowing the limits of how we actually work, having the self awareness and humility to be inquiring, and being able to think in terms of systems and root causes. Harris referenced the book Thinking in Systems by environmental scientist Donella Meadows, in which she details 12 leverage points for intervening in a system—that is, changing the way a system works from its current state to something else. In Harris’s opinion, the most relevant of Meadows’ points to the tech conversation is the power to transcend paradigms.

Each of the paradigms of thinking in the tech industry that got us where we are should be overhauled by a human-centered focus. Rather than shrugging off the harms of technology by asserting that there are always costs and benefits, we should focus on minimizing harmful externalities. Rather than giving users what they want, we must respect human weaknesses and vulnerabilities (for example, the way social media platforms exploit the brain’s dopamine response). Rather than maximizing personalization to give users a satisfying experience (also known as creating our own unique little echo chambers), we should strive to create shared understanding.

The question is, how do we get more people to go from being typical users of social media and other tech to being what Harris calls humane technologists?

It starts with raising awareness and educating ourselves. Harris and his team at the Center for Humane Technology created an online course called Foundations of Humane Technology, which takes registrants through six values-centered tenets that, if we prioritize them when designing new tech (or changing the design of existing tech), can improve our experience both individually and as an interconnected community.

“We would like to have 100,000 humane technologists who are trained in this new paradigm,” Harris said. “It’s hard to think about these things when you feel like you’re the only one asking these questions.”

We’re at an inflection point where it’s crucial for those working on technology to help create shared understanding; the world isn’t about to get less complex or volatile. On the contrary, Harris predicts we’re heading into a period of increasing global catastrophes fueled by climate change, inequality, and unstable political regimes, among other factors.

It’s a lot to take on, even a lot to contemplate. But, Harris said, he has hope because he’s seen the system change much faster in the last few years than ever before. People from within the tech industry have spoken out about the risks and harms of the products they helped create, from former YouTube engineer Guillaume Chaslot to Facebook co-founder Chris Hughes to former Facebook data scientist Frances Haugen, and many more. “Technologists are actually waking up and saying, ‘I don’t want to participate in the toxic part of the industry, I want to help build a better part,’” Harris said.

Going back to Wilson’s quote, Harris proposed the following: we need to embrace our Paleolithic emotions, upgrade our medieval institutions, and have the wisdom to wield our God-like technology. We need to be able to make sense of the world and have people from different sides come together and agree on the actions we should take—then take them. There should be no place for business models that are dependent on dividing people. “We need everyone working on helping us close that gap,” Harris said.

Image Credit: Rodion Kutsaev on Unsplash

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Posted in Human Robots

#439869 Short movie about Humanoid Androids

A short journey through the magical world of humanoid robots.

Posted in Human Robots

#438882 Robotics in the entertainment industry

Mesmer Entertainment Robotics demonstrate some of their humanoid animatronics, as well as their humanoid robot, Owen.

Posted in Human Robots

#439105 This Robot Taught Itself to Walk in a ...

Recently, in a Berkeley lab, a robot called Cassie taught itself to walk, a little like a toddler might. Through trial and error, it learned to move in a simulated world. Then its handlers sent it strolling through a minefield of real-world tests to see how it’d fare.

And, as it turns out, it fared pretty damn well. With no further fine-tuning, the robot—which is basically just a pair of legs—was able to walk in all directions, squat down while walking, right itself when pushed off balance, and adjust to different kinds of surfaces.

It’s the first time a machine learning approach known as reinforcement learning has been so successfully applied in two-legged robots.

This likely isn’t the first robot video you’ve seen, nor the most polished.

For years, the internet has been enthralled by videos of robots doing far more than walking and regaining their balance. All that is table stakes these days. Boston Dynamics, the heavyweight champ of robot videos, regularly releases mind-blowing footage of robots doing parkour, back flips, and complex dance routines. At times, it can seem the world of iRobot is just around the corner.

This sense of awe is well-earned. Boston Dynamics is one of the world’s top makers of advanced robots.

But they still have to meticulously hand program and choreograph the movements of the robots in their videos. This is a powerful approach, and the Boston Dynamics team has done incredible things with it.

In real-world situations, however, robots need to be robust and resilient. They need to regularly deal with the unexpected, and no amount of choreography will do. Which is how, it’s hoped, machine learning can help.

Reinforcement learning has been most famously exploited by Alphabet’s DeepMind to train algorithms that thrash humans at some the most difficult games. Simplistically, it’s modeled on the way we learn. Touch the stove, get burned, don’t touch the damn thing again; say please, get a jelly bean, politely ask for another.

In Cassie’s case, the Berkeley team used reinforcement learning to train an algorithm to walk in a simulation. It’s not the first AI to learn to walk in this manner. But going from simulation to the real world doesn’t always translate.

Subtle differences between the two can (literally) trip up a fledgling robot as it tries out its sim skills for the first time.

To overcome this challenge, the researchers used two simulations instead of one. The first simulation, an open source training environment called MuJoCo, was where the algorithm drew upon a large library of possible movements and, through trial and error, learned to apply them. The second simulation, called Matlab SimMechanics, served as a low-stakes testing ground that more precisely matched real-world conditions.

Once the algorithm was good enough, it graduated to Cassie.

And amazingly, it didn’t need further polishing. Said another way, when it was born into the physical world—it knew how to walk just fine. In addition, it was also quite robust. The researchers write that two motors in Cassie’s knee malfunctioned during the experiment, but the robot was able to adjust and keep on trucking.

Other labs have been hard at work applying machine learning to robotics.

Last year Google used reinforcement learning to train a (simpler) four-legged robot. And OpenAI has used it with robotic arms. Boston Dynamics, too, will likely explore ways to augment their robots with machine learning. New approaches—like this one aimed at training multi-skilled robots or this one offering continuous learning beyond training—may also move the dial. It’s early yet, however, and there’s no telling when machine learning will exceed more traditional methods.

And in the meantime, Boston Dynamics bots are testing the commercial waters.

Still, robotics researchers, who were not part of the Berkeley team, think the approach is promising. Edward Johns, head of Imperial College London’s Robot Learning Lab, told MIT Technology Review, “This is one of the most successful examples I have seen.”

The Berkeley team hopes to build on that success by trying out “more dynamic and agile behaviors.” So, might a self-taught parkour-Cassie be headed our way? We’ll see.

Image Credit: University of California Berkeley Hybrid Robotics via YouTube Continue reading

Posted in Human Robots