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On A Positive Note: Local students want to learn coding and robotics - Index-Journal

The facility provides more than 14,000 square miles of FAA-approved airspace for drone operations and testing

Verizon Robotics has chosen the Pendleton Unmanned Aerial Systems Range in Oregon as the new proving ground for its drone development.

The telecom giants robotics unit is expanding its operations and will base its advanced air R&D, drone partner and long-range robotics at the Pendleton Range, which offers more than 14,000 square miles of FAA-approved airspace for drone operations and testing.

Verizon Robotics will train Pendleton staff to operate a rapid-response command mobile-unit vehicle that can deploy mission-critical communications, applications and advanced computing solutions that can address air and ground robotics use cases.

The operations we are deploying at Pendleton Range will help advance R&D initiatives for customers, Verizon Robotics President Mariah Scott said. The ranges location helps create a local innovation testbed that can emulate real-life conditions, helping to bring products and services to market faster.

The northeastern Oregon facilitys mission is to facilitate the integration of UAVs into the national airspace system. It hosts between 400 to 1,000 test-and-learn operations per month and handles both drones and ground robotics.

The Pendleton Range is excited to be working with Verizon Robotics to provide state-of-the-art situational-awareness technology to one of the busiest UAV test ranges in the U.S., Pendleton manager Darryl Abling said. This investment will help to develop technologies and processes that will help accelerate overall aviation innovation as robotics become more integrated into the National Airspace System.

This week, Verizon Robotics is participating in the AUVSI XPONENTIAL autonomous technology convention in Orlando, Fla., where it will conduct live demonstrations of its tech and provide speakers to two events.

Were seeing demand for robotics jump as more organizations are moving into robotics to address labor shortages, maximize resources and differentiate themselves from the competition, Scott said. AUVSI serves as a meeting point for enterprises that are serious about having a competitive advantage through robotics in the marketplace.

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Verizon Robotics to Test Drones at Oregon's Pendleton Range - IoT World Today

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Global Minimally Invasive Medical Robotics, Imaging and Visualization Systems and Surgical Instruments Market 2022 Growth Statistics by Key Vendors ...

Students in Franklin Community High Schools percussion band and Center Grove High Schools robotics team were part of history last month.

Franklins percussion band finished fourth in its class during the Winter Guard International World Championships in Dayton, Ohio, the best an Indiana school finished in its class. Center Groves robotics team made it to the Championship Division finals in the FIRST Championship, a world championship in Houston, also the best finish for an Indiana school.

Both competitions took place during the week of April 18.

The Center Grove High School robotics team finished as Championship Division finalists at the FIRST Championship, a world championship in Houston.

SUBMITTED PHOTO

The Franklin Community High School percussion band finished fourth at the Winter Guard International World Championships in Dayton.

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L-R, Center Grove High School junior Grant Embrey and senior Cade Winiger work on a robot April 12 at the schools innovation center. The robotics team finished as Championship Division finalists at the FIRST Championship, a world championship in Houston.

DAILY JOURNAL FILE PHOTO

Center Grove High School junior Mitul Patel works on a drive train part for the base of a robot April 12 at Center Grove High Schools Innovation Center. The robotics team finished as Championship Division finalists at the FIRST Championship, a world championship in Houston.

DAILY JOURNAL FILE PHOTO

The Franklin Community High School percussion band rehearses on April 11. The band finished fourth at the Winter Guard International World Championships in Dayton.

DAILY JOURNAL FILE PHOTO

The Franklin Community High School percussion band rehearses on April 11. The band finished fourth at the Winter Guard International World Championships in Dayton.

DAILY JOURNAL FILE PHOTO

The Franklin Community High School percussion band rehearses on April 11. The band finished fourth at the Winter Guard International World Championships in Dayton.

DAILY JOURNAL FILE PHOTO

For the robotics competition, students traveled to Houston via a 15-hour charter bus ride from Center Grove High School. For many students, one of the highlights of the trip involved not the robotics itself, but who they met, which included students from Israel, Hungary, India, Turkey, Mexico, Canada, Taiwan, Switzerland, Japan, Australia and China, said Brina Porat, operations captain.

It was a really amazing experience, Porat said. Three were teams from different countries that shared the same passion we did. A lot of them gave out handouts in their team pits so that you got a piece of their culture. I remember coming home with bags and bags of handouts from other countries. I have hundreds of buttons from other countries, and its a really amazing keepsake.

The robotics team ended up falling just short of the championship round, but the experience was unforgettable for seniors such as Eli Leser, the teams executive captain.

It was really great to go out there and see some of the best teams from all over the world. The team from Israel ended up being no. 2. It was great to see all these ideas and cultures were brought together by this one organization to play this one game. It was great to see how FIRST has inspired and improved the lives of so many people and how much everyone is learning from FIRST.

The team represents not only the school, but the entire community, said Lisa Porat, president of the Red Alert Robotics Parent Organization.

It was one of the most rewarding things I had done as a parent and a mentor, to be able to see these kids in that atmosphere and experience, Lisa Porat said. This was worth every effort we put into it and you couldnt have asked for a better opportunity to shine for our team and the community. I was so proud of these kids, and they made such as impact on our lives.

The percussion band, after a lackluster finish at the state finals, saved its best for last, almost reaching the podium in the WGI World Championships. The fourth place finish was the best ever placement for the percussion band, which previously only went to the world championships in 2019, when they were eliminated in the preliminary rounds.

A total of 47 teams across the United States competed in Franklin High Schools class, with Franklin the only Indiana band to crack the top five, said Jason Hammond-Wood, the bands director.

Every aspect of our show improved. What we got better at and what stuck out at the national level was the nuance in our show design, and our musicality shines through when were performing at the highest level, Hammond-Wood said. With 50 groups that all stick out, what makes you stick out? Maturity and musicianship.

The percussion bands performance, titled Heart-Shaped Box, revolved around community and a wide range of emotions. In the beginning of the show, the heart is in a corner, representing feeling trapped, followed by a somber segment, during which the community crumbles and everyone is holding smaller, individual pieces. At the end of the show, the students decide to work together and unveil a larger heart.

The band bought into its performance more, which helped make it more authentic, said Bella Street, a senior in her third year in percussion band.

I felt we bought into it a lot more, Street said. We were just playing it safe at state, but we had three weeks to clean our show and got used to it. I think we finally learned what the show meant to us and put in our best effort. To me, it meant a lot. Its my last year and this is a great note to end on.

Ethan Hacker, also a senior, served as the front ensemble section leader, and has been in band since sixth grade.

Going into the final rounds, I thought of it as any other competition; Ive done this hundreds of times before, but all my effort, my mental effort was to make sure other people were doing as well as myself, Hacker said. It was awesome. I was excited last year to get second at state, but to get fourth in the nation, its just so much better.

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Local percussion band, robotics team have greatest ever finishes - Daily Journal

During the 2020-21 school year, its easy to imagine how hard it was to achieve everything with half of her students at home. But Tseng still made it work. She met with at-home students regularly on Zoom and discussed what they were going to do and how they could still help the team. Despite all obstacles, the teams still made it to both the state and world competitions, even winning an award at VEX Worlds.

Her death was a shock to the entire school and has been difficult to overcome, but her Robotics Club students did not let that stop them from participating in competitions because thats what Tseng wouldve wanted. The biggest obstacle isnt the drive to compete, but who will sponsor it? Since Tseng was the only STEM/robotics teacher, there was no one who knew the programs ins and outs. No one on campus knew how to plan and keep track of events. Who was going to lead and guide now?

But the students and staff at Hill came together in honor of Tseng and have persevered, thanks to several teachers jumping in to help.

Hill Middle School is going to keep Tsengs legacy and the STEM program going. It is in the process of hiring a new teacher, and currently interviewing incoming students interested in STEM and robotics. Current students and teachers are working together to make this happen.

Despite all of this, several teams have made accomplishments in competitions this year. Teams 505B (eighth-grade girls) and 505C (eighth-grade boys) made it to the national competition, and team 505T (seventh-grade girls) made it to the VEX Robotics Worlds competition for the 2021-22 school year.

The robotics teams will continue to win not for themselves, but for Tseng.

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How the Robert T. Hill robotics team persisted after their teacher's death - Lakewood/East Dallas Advocate

EAST PALO ALTO, CALIFORNIA APRIL 25: Cristina Becerra,17, and Nathan Valencia,16, get ready to shoot a ball from their robot using The Butter Duster, a mechanism that shoots balls to help defeat their opponents in competition, in East Palo Alto, Calif., Monday, April 25, 2022. (Shae Hammond/Bay Area News Group)

EAST PALO ALTO In a small garage on Beech Street, saw-on-metal screeches bounce off the walls and spill out, interrupted only by the sounds of mechanical movements and the faint murmur of kids happily barking orders at each other while working on their collective pet project.

For the past three years, a dozen-or-so East Palo Alto middle- and high-schoolers have been experimenting with basic motors, electrical engineering, computer vision, hydraulics, machining, pneumatics and software development with one goal in mind: Build a robot.

Palo Alto High School Senior Cristina Becerra, who is one of the longest-running members of East Palo Alto Robotics, said the team just kind of happened. She prefers to call the team the Churrobots (they hang tiny churros off of their robot honoring their largely Hispanic roots).

Without many options around her in East Palo Alto, she and a few dedicated people cobbled together enough parts and equipment from Home Depot and other local teams to forge it out of nothing.

In Silicon Valley,youd be hard-pressed to find a school thatdoesnt have a robotics program (all the techie parents have made sure of that) competing with the best of equipment and teams of talented young software and hardware engineers.

But in underserved East Palo Alto which for decades has struggled with underfunded education, high poverty and the myriad issues that face its largely Hispanic immigrant community dedicating the money and time to building a robot just isnt as common and students are forced to visit Palo Alto or Mountain View in search of after-school programs that cater to their automaton interests.

Becerra said she first started building machines in 4th grade, using mainly Legos to build rudimentary robots.

We bought a kit of parts to build a drive train and built with a bunch of Legos, Becerra said. I thought it was really fun, so I decided to try it out and I ended up liking it. But then we aged out of it so we decided to make a team for high schoolers so the students who aged out could join and still continue robotics.

The teams challenges have grown since its junior high days, with frequent regional competitions serving as deadlines for building a robot able to stand a chance at the First Robotics Competition, an international high school robotics contest held every year since 1992.

Each year, teams of high school students, coaches and mentors work during a six-week period to build robots capable of competing in that years game. The robots must weigh no more than 125 pounds, and theyre judged based on changing standards every year. The robots must perform tasks like scoring balls into goals, placing inner tubes onto racks, hanging on bars or balance on beams.

The competition boasts 3,225 teams from over 34 countries and regions as of this year, and East Palo Alto Robotics believes theyre probably the scrappiest.

Greg Corsetto, one of the coaches who helped start the team seven years ago said the whole East Palo Alto Robotics experience is very Silicon Valley. They work out of Coach Matt Pizzimentis garage, which he volunteered for the team to have a dedicated workspace and mostly rely on Home Depot parts and hand-me-down equipment to make it all happen.

It feels like were starting our own company or something, said Becerrra.

But its not the fun and games youd expect. Pizzimenti said the students do a ton to balance between school and robotics and other things in life.

In the weeks between our first competition and the second, Im pretty confident we worked something like 30-hour weeks, Pizzimenti said. Im honestly mind-blown.

For Pizzimenti, seeing the kids grow up is as much of a reward. Hes been coaching students like Becerra since she was 9.

Its so crazy remembering her as a fourth-grader doing Legos and seeing who she is today, not just in terms of her capabilities in building a robot but also shes our captain, shes grown in her leadership role. he said. . Shes gone from a very opinionated 4-grader to a still-very-opinionated senior who has developed keen leadership skills.

The team, however, doesnt do very well. They got 52 out of 59 in the Silicon Valley regional competition this year. But what they lack in experience and success they make up for in grit, dedication and an understanding that it takes time to build something truly great.

Its kind of sad to see your team lose basically every match, but it was also really exciting seeing all the hard work we put in. We were at Matts until 11:30 p.m. and midnight just trying to get it perfect, Becerra said. We did a lot with what we had. And even though we kind of knew we werent going to win, that didnt take away from anything. We were still really happy with how the robot turned out, and we grew as a team because of it.

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Its like were starting our own company East Palo Alto high-schoolers building robots with spare parts out of coachs garage - The Mercury News

The ability to make decisions autonomously is not just what makes robots useful, it's what makes robots robots. We value robots for their ability to sense what's going on around them, make decisions based on that information, and then take useful actions without our input. In the past, robotic decision making followed highly structured rulesif you sense this, then do that. In structured environments like factories, this works well enough. But in chaotic, unfamiliar, or poorly defined settings, reliance on rules makes robots notoriously bad at dealing with anything that could not be precisely predicted and planned for in advance.

RoMan, along with many other robots including home vacuums, drones, and autonomous cars, handles the challenges of semistructured environments through artificial neural networksa computing approach that loosely mimics the structure of neurons in biological brains. About a decade ago, artificial neural networks began to be applied to a wide variety of semistructured data that had previously been very difficult for computers running rules-based programming (generally referred to as symbolic reasoning) to interpret. Rather than recognizing specific data structures, an artificial neural network is able to recognize data patterns, identifying novel data that are similar (but not identical) to data that the network has encountered before. Indeed, part of the appeal of artificial neural networks is that they are trained by example, by letting the network ingest annotated data and learn its own system of pattern recognition. For neural networks with multiple layers of abstraction, this technique is called deep learning.

Even though humans are typically involved in the training process, and even though artificial neural networks were inspired by the neural networks in human brains, the kind of pattern recognition a deep learning system does is fundamentally different from the way humans see the world. It's often nearly impossible to understand the relationship between the data input into the system and the interpretation of the data that the system outputs. And that differencethe "black box" opacity of deep learningposes a potential problem for robots like RoMan and for the Army Research Lab.

In chaotic, unfamiliar, or poorly defined settings, reliance on rules makes robots notoriously bad at dealing with anything that could not be precisely predicted and planned for in advance.

This opacity means that robots that rely on deep learning have to be used carefully. A deep-learning system is good at recognizing patterns, but lacks the world understanding that a human typically uses to make decisions, which is why such systems do best when their applications are well defined and narrow in scope. "When you have well-structured inputs and outputs, and you can encapsulate your problem in that kind of relationship, I think deep learning does very well," says Tom Howard, who directs the University of Rochester's Robotics and Artificial Intelligence Laboratory and has developed natural-language interaction algorithms for RoMan and other ground robots. "The question when programming an intelligent robot is, at what practical size do those deep-learning building blocks exist?" Howard explains that when you apply deep learning to higher-level problems, the number of possible inputs becomes very large, and solving problems at that scale can be challenging. And the potential consequences of unexpected or unexplainable behavior are much more significant when that behavior is manifested through a 170-kilogram two-armed military robot.

After a couple of minutes, RoMan hasn't movedit's still sitting there, pondering the tree branch, arms poised like a praying mantis. For the last 10 years, the Army Research Lab's Robotics Collaborative Technology Alliance (RCTA) has been working with roboticists from Carnegie Mellon University, Florida State University, General Dynamics Land Systems, JPL, MIT, QinetiQ North America, University of Central Florida, the University of Pennsylvania, and other top research institutions to develop robot autonomy for use in future ground-combat vehicles. RoMan is one part of that process.

The "go clear a path" task that RoMan is slowly thinking through is difficult for a robot because the task is so abstract. RoMan needs to identify objects that might be blocking the path, reason about the physical properties of those objects, figure out how to grasp them and what kind of manipulation technique might be best to apply (like pushing, pulling, or lifting), and then make it happen. That's a lot of steps and a lot of unknowns for a robot with a limited understanding of the world.

This limited understanding is where the ARL robots begin to differ from other robots that rely on deep learning, says Ethan Stump, chief scientist of the AI for Maneuver and Mobility program at ARL. "The Army can be called upon to operate basically anywhere in the world. We do not have a mechanism for collecting data in all the different domains in which we might be operating. We may be deployed to some unknown forest on the other side of the world, but we'll be expected to perform just as well as we would in our own backyard," he says. Most deep-learning systems function reliably only within the domains and environments in which they've been trained. Even if the domain is something like "every drivable road in San Francisco," the robot will do fine, because that's a data set that has already been collected. But, Stump says, that's not an option for the military. If an Army deep-learning system doesn't perform well, they can't simply solve the problem by collecting more data.

ARL's robots also need to have a broad awareness of what they're doing. "In a standard operations order for a mission, you have goals, constraints, a paragraph on the commander's intentbasically a narrative of the purpose of the missionwhich provides contextual info that humans can interpret and gives them the structure for when they need to make decisions and when they need to improvise," Stump explains. In other words, RoMan may need to clear a path quickly, or it may need to clear a path quietly, depending on the mission's broader objectives. That's a big ask for even the most advanced robot. "I can't think of a deep-learning approach that can deal with this kind of information," Stump says.

While I watch, RoMan is reset for a second try at branch removal. ARL's approach to autonomy is modular, where deep learning is combined with other techniques, and the robot is helping ARL figure out which tasks are appropriate for which techniques. At the moment, RoMan is testing two different ways of identifying objects from 3D sensor data: UPenn's approach is deep-learning-based, while Carnegie Mellon is using a method called perception through search, which relies on a more traditional database of 3D models. Perception through search works only if you know exactly which objects you're looking for in advance, but training is much faster since you need only a single model per object. It can also be more accurate when perception of the object is difficultif the object is partially hidden or upside-down, for example. ARL is testing these strategies to determine which is the most versatile and effective, letting them run simultaneously and compete against each other.

Perception is one of the things that deep learning tends to excel at. "The computer vision community has made crazy progress using deep learning for this stuff," says Maggie Wigness, a computer scientist at ARL. "We've had good success with some of these models that were trained in one environment generalizing to a new environment, and we intend to keep using deep learning for these sorts of tasks, because it's the state of the art."

ARL's modular approach might combine several techniques in ways that leverage their particular strengths. For example, a perception system that uses deep-learning-based vision to classify terrain could work alongside an autonomous driving system based on an approach called inverse reinforcement learning, where the model can rapidly be created or refined by observations from human soldiers. Traditional reinforcement learning optimizes a solution based on established reward functions, and is often applied when you're not necessarily sure what optimal behavior looks like. This is less of a concern for the Army, which can generally assume that well-trained humans will be nearby to show a robot the right way to do things. "When we deploy these robots, things can change very quickly," Wigness says. "So we wanted a technique where we could have a soldier intervene, and with just a few examples from a user in the field, we can update the system if we need a new behavior." A deep-learning technique would require "a lot more data and time," she says.

It's not just data-sparse problems and fast adaptation that deep learning struggles with. There are also questions of robustness, explainability, and safety. "These questions aren't unique to the military," says Stump, "but it's especially important when we're talking about systems that may incorporate lethality." To be clear, ARL is not currently working on lethal autonomous weapons systems, but the lab is helping to lay the groundwork for autonomous systems in the U.S. military more broadly, which means considering ways in which such systems may be used in the future.

The requirements of a deep network are to a large extent misaligned with the requirements of an Army mission, and that's a problem.

Safety is an obvious priority, and yet there isn't a clear way of making a deep-learning system verifiably safe, according to Stump. "Doing deep learning with safety constraints is a major research effort. It's hard to add those constraints into the system, because you don't know where the constraints already in the system came from. So when the mission changes, or the context changes, it's hard to deal with that. It's not even a data question; it's an architecture question." ARL's modular architecture, whether it's a perception module that uses deep learning or an autonomous driving module that uses inverse reinforcement learning or something else, can form parts of a broader autonomous system that incorporates the kinds of safety and adaptability that the military requires. Other modules in the system can operate at a higher level, using different techniques that are more verifiable or explainable and that can step in to protect the overall system from adverse unpredictable behaviors. "If other information comes in and changes what we need to do, there's a hierarchy there," Stump says. "It all happens in a rational way."

Nicholas Roy, who leads the Robust Robotics Group at MIT and describes himself as "somewhat of a rabble-rouser" due to his skepticism of some of the claims made about the power of deep learning, agrees with the ARL roboticists that deep-learning approaches often can't handle the kinds of challenges that the Army has to be prepared for. "The Army is always entering new environments, and the adversary is always going to be trying to change the environment so that the training process the robots went through simply won't match what they're seeing," Roy says. "So the requirements of a deep network are to a large extent misaligned with the requirements of an Army mission, and that's a problem."

Roy, who has worked on abstract reasoning for ground robots as part of the RCTA, emphasizes that deep learning is a useful technology when applied to problems with clear functional relationships, but when you start looking at abstract concepts, it's not clear whether deep learning is a viable approach. "I'm very interested in finding how neural networks and deep learning could be assembled in a way that supports higher-level reasoning," Roy says. "I think it comes down to the notion of combining multiple low-level neural networks to express higher level concepts, and I do not believe that we understand how to do that yet." Roy gives the example of using two separate neural networks, one to detect objects that are cars and the other to detect objects that are red. It's harder to combine those two networks into one larger network that detects red cars than it would be if you were using a symbolic reasoning system based on structured rules with logical relationships. "Lots of people are working on this, but I haven't seen a real success that drives abstract reasoning of this kind."

For the foreseeable future, ARL is making sure that its autonomous systems are safe and robust by keeping humans around for both higher-level reasoning and occasional low-level advice. Humans might not be directly in the loop at all times, but the idea is that humans and robots are more effective when working together as a team. When the most recent phase of the Robotics Collaborative Technology Alliance program began in 2009, Stump says, "we'd already had many years of being in Iraq and Afghanistan, where robots were often used as tools. We've been trying to figure out what we can do to transition robots from tools to acting more as teammates within the squad."

RoMan gets a little bit of help when a human supervisor points out a region of the branch where grasping might be most effective. The robot doesn't have any fundamental knowledge about what a tree branch actually is, and this lack of world knowledge (what we think of as common sense) is a fundamental problem with autonomous systems of all kinds. Having a human leverage our vast experience into a small amount of guidance can make RoMan's job much easier. And indeed, this time RoMan manages to successfully grasp the branch and noisily haul it across the room.

Turning a robot into a good teammate can be difficult, because it can be tricky to find the right amount of autonomy. Too little and it would take most or all of the focus of one human to manage one robot, which may be appropriate in special situations like explosive-ordnance disposal but is otherwise not efficient. Too much autonomy and you'd start to have issues with trust, safety, and explainability.

"I think the level that we're looking for here is for robots to operate on the level of working dogs," explains Stump. "They understand exactly what we need them to do in limited circumstances, they have a small amount of flexibility and creativity if they are faced with novel circumstances, but we don't expect them to do creative problem-solving. And if they need help, they fall back on us."

RoMan is not likely to find itself out in the field on a mission anytime soon, even as part of a team with humans. It's very much a research platform. But the software being developed for RoMan and other robots at ARL, called Adaptive Planner Parameter Learning (APPL), will likely be used first in autonomous driving, and later in more complex robotic systems that could include mobile manipulators like RoMan. APPL combines different machine-learning techniques (including inverse reinforcement learning and deep learning) arranged hierarchically underneath classical autonomous navigation systems. That allows high-level goals and constraints to be applied on top of lower-level programming. Humans can use teleoperated demonstrations, corrective interventions, and evaluative feedback to help robots adjust to new environments, while the robots can use unsupervised reinforcement learning to adjust their behavior parameters on the fly. The result is an autonomy system that can enjoy many of the benefits of machine learning, while also providing the kind of safety and explainability that the Army needs. With APPL, a learning-based system like RoMan can operate in predictable ways even under uncertainty, falling back on human tuning or human demonstration if it ends up in an environment that's too different from what it trained on.

It's tempting to look at the rapid progress of commercial and industrial autonomous systems (autonomous cars being just one example) and wonder why the Army seems to be somewhat behind the state of the art. But as Stump finds himself having to explain to Army generals, when it comes to autonomous systems, "there are lots of hard problems, but industry's hard problems are different from the Army's hard problems." The Army doesn't have the luxury of operating its robots in structured environments with lots of data, which is why ARL has put so much effort into APPL, and into maintaining a place for humans. Going forward, humans are likely to remain a key part of the autonomous framework that ARL is developing. "That's what we're trying to build with our robotics systems," Stump says. "That's our bumper sticker: 'From tools to teammates.' "

This article appears in the October 2021 print issue as "Deep Learning Goes to Boot Camp."

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What happened

Shares in energy-focused engineering services and subsea robotics company Oceaneering International (OII -2.07%) fell nearly 10% in the week to Friday morning. The move came after investors took a dim view of the company's first-quarter earnings release.

It's not that there was anything wrong with the earnings report itself, but instead that it was not good enough to justify the optimism the market had built into the stock previously. Oceaneering generates roughly 75% of its earnings from the energy industry (subsea robotics, remotely controlled vehicles for offshore projects, robotic inspection of energy assets) with the rest coming from robotics and automation in aerospace and defense, entertainment systems (theme parks, airports), and industrial applications.

Image source: Getty Images.

The stock's prospects receive a boost when the outlook improves for energy-related spending, particularly for offshore oil and gas spending. In this context, it's not hard to see why investors have crowded into the stock in 2022. With the price of oil rising to above $100 a barrel and oil market dislocations occurring due to the war in Ukraine, energy companies are under pressure to increase capital spending.

Indeed, oil services giant Halliburton recently upgraded its expectations for customer spending in North America in 2022.

In this environment, investors probably expected a bit more from Oceaneering than merely maintaining full-year earnings and free cash flow guidance. For reference, management continues to guide toward consolidated earnings before interest, taxes, depreciation, and amortization (EBITDA) of $225 million to $275 million and free cash flow (FCF) of $75 million to $125 million.

CEO Roderick Larson says that "market conditions continue to be supportive of a robust ramp-up in activity and pricing improvements beginning in the second quarter and continuing for the remainder of the year." However, it's not enough to lead management to increase its earnings guidance

While investors didn't get the guidance hike they were hoping for, it's worth noting that the midpoint of the guidance puts Oceaneering stock on a forward enterprise value (market cap plus net debt) to EBITDA multiple of 5.2 times and a price to FCF of 11.6 times. Those are very attractive multiples if you believe in the company's long-term growth prospects.

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Local NewsPolson Robotics Team Headed to World Championships

By By Ben Rayner 04/27/2022 08:13 a.m. EST

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Though the robotics teams from Polson Middle School has only been competing for a few short years, its already making a name for itself. One of its teams competing this year, Squad 1695-E, has won a spot and is headed to Texas in early May to vie for the Vex World Championships in Team robotics.

The Polson Robotics Team has several different squads led by Team Advisor Dan Grenier, who is also the career and technical program advisor at Daniel Hand High School. According to Grenier, all of the teams four-person squads qualified for the State Robotics Championship competition in Plainville in March.

At that competition, the 1695-E squad composed of four 8th-grade studentsJosh Lee, Jack Varone, Brennen Catino, and Emma Mannixreceived the Think Award, which entitled them to compete in the Vex Robotics World Championships in Dallas, Texas, this May. No other Madison team has ever made it to the World Championships, said Grenier.

According to Grenier, the invite is a very special and prestigious one for the students.

Its a big deal. There are 800 hundred teams that go to this event from around the world. So, you have your local matches and depending on how you qualify then you go to your regional or state competition, Grenier said. And depending on well you do there, you can then qualify for the worlds.

The students are beyond excited, maybe a bit overwhelmed too, I think, he continued. It is such a great experience for them. In robotics, it is competitive, but there is a whole spirit of helping each other. At any event if something ever goes wrong, or your robot falls apart, there is another team ready to step in and help you out. So, it is that kind of atmosphere, teamwork, and that cooperation, is really what robotics is all about.

According to Grenier, the competition is important but the educational and life skills development of the students is the most critical aspect of this challenge.

Part of what we are doing is trying to create opportunities for students to explore the fields of engineering and technology. Not every student is an athlete and will play sports. So, this is a great opportunity to get that type of experience and still build skills and knowledge that could benefit them in a career after high school, Grenier said.

The name of Squad 1695-Es robot is VEXimithiosoarus IQuithis, and the students are extremely excited about the big competition, according to Polson Principal Kathryn Hart.

I think it is important to have choices for kids, so that they can get involved in a variety of activities, said Hart. The kids have gained a lot from it. Its about working together, by learning about the technology behind things. And they are really looking forward to going to the championships. That is a really big deal for kids in middle school. Its huge. They are really excited and are also learning about fundraising as well. Bake sales, candy sales, they have been really working on that aspect as well, to help pay for the trip. They have all worked really hard and really well together to bring this all to fruition.

The Think Award, which was the honor that got the team invited, is a judged award and goes to the team that best displays the removal of engineering obstacles through creative thinking. It is given to the team that best reflects the journey the team took as it experienced the engineering design process during the robotic build season, according to Carolyn Varone, parent to team member Jack Varone.

Team member Mannix said the robotics team experience has been rewarding for all of the students who participate.

The Robotics Club is a ton of fun. Even if you dont have a ton background of background knowledge, its very inclusive and everyone is really nice, said Mannix. The Robotics Club helps grow independent thinking and prepares you for a future because we dont get help from Mr. Grenierwe have to figure it out ourselves. Everything weve done weve done without help, and that really helps prepare us for the future.

According to Mannix, the experience prepares students like her for future math and science courses via a nontraditional and fun process. Mannix said the team is excited and is looking forward to the challenge, but the camaraderie and spirit of cooperation are what make the events special for her and her team mates

We are all very excited, yeah. Well try and win, but thats not everything, Mannix said. The other teams have helped us, and weve helped other teams. So, it is very much about teamwork and everyone helping each other. Its a great community.

The team is seeking donations to help defray the costs associated with their travel to Texas for the competition. The group has held several fundraisers, but is seeking support from the public in these efforts. Anyone wishing to donate can call Polson Middle School at 203-245-6480 and arrange to submit a donation. There will also be a fundraiser at Amatos Pizza on Wednesday, May 4 as well.

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Polson Robotics Team Headed to World Championships - Zip06.com

Usage of robots in everyday life used to look futuristic and that too is most commonly in Hollywood movies. When we think about the usage of robots in everyday life, we cannot help but visualize robots in the form of humanoids that carry out tasks just like humans to simplify their life.

With the advancement of technology, robotic application in everyday life is no longer futuristic, it is the truth of the present world. Robots are generally used in everyday life which are considered boring, repeated and dangerous.

Robots were extremely helpful for the experts and now, they not only serve these experts but also the common people. Here are five ways in which robots are used in everyday lives.

When it comes to the use of robots in everyday life, one cannot deny its application in robots, especially in Japan. In fact, in Japan, the usage of robots is extremely prevalent. Robots in Japan are commonly employed in jobs like chopping vegetables and making sushi.

They also play a critical role in accessing the farmers in producing food, especially in planting rice and growing other crops. In addition to that, robots are also used in making tees.

However, for using the power of the internet in controlling a robot, a high internet speed is a must. You can check the current speed of the internet on Speed Test and Verizon. Also, robots are used as barmen and are employed to serve drinks.

Robots are also used in the health sector for medical training of doctors, paramedics and nurses. The robots in medical training are powerful enough to simulate different medical conditions.

The students who are training themselves to become future doctors can then work on these training robots to practice the diagnosis and treatment of different diseases.

These training robots work similarly to flight simulators. The size of these robots can vary in sizes depending upon the task that they need to do.

The primary objective of designing a robot is to make the life of the human easier. Therefore, using robots in managing household chores is a must when it comes to the application of robots in everyday life.

In the house, robots can serve different functions like a lawn-mower, vacuum bots and cooking bots. Robots particularly help in cleaning the housework toward comforting the mundane and tough task of cleaning the house.

These robots use artificial intelligence to function. Also, these robots sometimes use machine learning to improve their efficiency.

Robots are also now programmed to assist elderly people who live in assisted care facilities. A Korean robot that is designed to assist living is powerful enough to carry a human weighing 220 pounds.

These robots are easy to control and most of the time, they can be controlled easily with a joystick. The robots are designed to assist the elderly people to help the elderly to give them a sense of friendship and carry out their everyday activities.

Cleaning the sewer system is one of the dirtiest and most harmful tasks. Manually cleaning the sewer system is very problematic and downright dangerous. Robots can be used to manage the sewer.

In addition to cleaning the sewer system, the robots can also be used to inspect and maintain the sewer. These have eased the task of the inspection workers by many folds.

These are a few of the many tasks which robots can now do to help people in everyday life. The robotic revolution is nowhere. There are many ways in which robots can help one in everyday life.

This is not the first time that robots have become a part of everyday life. Robots have always played an important role but now their importance is realized in a better manner.

Not only the common people are using robots to simplify the task but also, their application in the industrial world is also huge. The robotic revolution is here to stay and in fact, will keep growing with each passing day.

The technological advancement will further improve the working of the robots and its importance will further be realized by everyone. In the future, robots will be highly used to complete mundane and dangerous tasks for sure.

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5 Ways in Which Robots are Used In Everyday Lives - Robotics and Automation News