The robotics industry is ever-changing. Engineers are always looking for new ways to create helpful tools and machinery, but those employees are often primarily men.

From action heroes to villainous assassins, biohybrid robots made of both living and artificial materials have been at the center of many sci-fi fantasies, inspiring today's robotic innovations. It's still a long way until human-like robots walk among us in our daily lives, but scientists from Japan are bringing us one step closer by crafting living human skin on robots. The method developed, presented June 9 in the journal Matter, not only gave a robotic finger skin-like texture, but also water-repellent and self-healing functions.

There are up to 66 million tons of waste in our oceans today, and the overwhelming majority of it is found on the ocean floor. However, with the exception of a few potentially dangerous operations using human divers, most endeavors to tackle seabed waste have focused on addressing litter floating on the surface. Researchers from the EU-funded SeaClear project are developing an AI-based solution for cleaning up the ocean floor without putting human lives at risk.

A horse, a zebra and artificial intelligence helped a team of Carnegie Mellon University researchers teach a robot to recognize water and pour it into a glass.

A team of researchers at Korea Advanced Institute of Science and Technology, working with one colleague from MIT and another from the University of Stuttgart, has developed a biomimetic elastomeric robot skin that has tactile sensing abilities. Their work has been published in the journal Science Robotics.

Prior to the upgrade the process involved manual removal and depositing of bread packages, which was potentially error prone and certainly not as efficient. The team at KINE knew that machine vision was the only possible solution for this type of system.

Blake Resnick was a 17-year-old growing up in Las Vegas when a gunman firing from a Strip hotel window killed 60 people at an outdoor concert.

Technologists from Accenture, NVIDIA, General Motors, Landing AI and Intel will offer insights and best practices on how businesses can take advantage of AI to enhance their automation processes and improve efficiencies and productivity overall.

Graduate students at the Georgia Institute of Technology have built the first graffiti-painting robot system that mimics the fluidity of human movement. Aptly named GTGraffiti, the system uses motion capture technology to record human painting motions and then composes and processes the gestures to program a cable-driven robot that spray paints graffiti artwork.

A paper published in the April 2022 issue of IEEE Robotics and Automation Letters outlines the AMBER team's method and represents the first instance of combining hybrid zero dynamics (HZD)—a mathematical framework for generating stable locomotion—with a musculoskeletal model to control a robotic assistive device for walking.

This article is a cutout of my forthcoming book that you can sign up for here: https://www.danrose.ai/book

When interviewing domain experts for artificial intelligence solutions, it's essential to avoid discussing a specific solution but instead focus on the business outcome and the problem at hand. When you interview experts, they sometimes settle on a particular solution too early, even without knowing it. As the solution architect, you might also do the same and miss out on better alternatives. I often catch myself doing that as finding the perfect solution is the most satisfying part of the discovery phase. To focus on the problem and business outcome, I use the following guide as inspiration for questions.

Question: Tell me about the last time you did X (E.g. forecasted sales or did shift planning at the ice cream store)

The question works better than "How do you do forecasting?". Asking this way will provide you with a polished best-case answer. The subject matter expert will tell you how everything is supposed to be done. We all want to present our best version of ourselves, and we can be a little afraid of admitting that we jump hoops when we are busy or things are a little messy. But we are all busy, and everyday work is messy. Teresa Torres has a great example in her book "Continues Discovery Habits.": When you ask people how they buy jeans, they will tell you that they go by brand and quality. When you ask them how they bought jeans the last time, they will tell you that there was a nice discount. 

When building AI, you are looking to identify all the mess and procedure bypassing. That is where you will face challenges, and can you decrease these with AI; you can provide much value.

Question: How will you use the information provided by the AI? (E.g. Information about how many ice creams are sold on a given day)

That question focuses on the business need and outcome and not just the wish for the information or the technical solution. The value in any AI can be found in what action we decide on based on the information provided by the model. Uncovering the intended actions reveals the potential value of the AI solution. It also exposes the reasoning (And sometimes the lack of) behind the need for the AI solution.

Question: How would the solution help your new colleague?

Experienced employees can have a hard time seeing the idea of assistance (from AI or not). They can always find a solution to challenges. They don't need help. But when their inexperienced colleagues become the subject, they have an easier time seeing the value and can explain how a solution will help them.

Question: Why can't you solve this problem in any other way than AI?

That will often result in the subject telling you how they think AI will solve the problem. It uncovers potential misunderstandings about what AI can and cannot do.

It also uncovers how well thought through the idea is. Is AI just solutions chosen due to the hype, or have alternatives seriously been considered? Don't be afraid to challenge the idea of using AI. Any good decision can stand that test and is it not a good decision, you will know at some point no matter what. Better sooner than later.

Question: Why will this solution fail?

Have you ever heard people say: "I knew that would fail"? If that is true, even occasionally, then asking this question can save you trouble. You might also know the feeling that you ignored the signs of challenges when you were too excited about a solution. I certainly do.

When asking this question, I often get the answer: "We will fail because we will try to solve everything and not get it done." That is a usual challenge and making the subjects say this brings some realism to the project.

Question: Show me how you do X?

Make the person show you how they do their work. Observing a subject's actions will uncover intangible knowledge. What has become type 1 and routine for the subject will confuse you, and you can point that out and ask what is going on.

Question: What will be hard about (X, Y, Z)?

I often ask questions such as "What will be hard about getting a high accuracy?" or "What will be hard about onboarding users to the solution?". Questions like that uncovers will uncover data features that might not be as trustworthy as you thought. Answers like "We changed the way we log data for X recently" are typical here.

For tips, sign up for the book here: https://www.danrose.ai/book

Tesla’s automation strategy has shifted over the last five years. By investigating where Tesla made mistakes and where it excelled, the reader will benefit from Tesla’s hard-earned lessons and gain an understanding of how to build an automation strategy.

Dr. Sihao Sun discusses his award-winning research in the area of controlling the flight of a drone when faced with a sudden rotor failure.

Typical research in this area addressed the case where one of the four rotors in a quadrotor suddenly, spontaneously stops working. This previous research does not take into full account real-life scenarios where rotor failure is common. This includes collisions with other drones, walls, birds, and operating in degraded GPS environments.

Dr. Sihao Sun

Dr. Sihao Sun is a postdoctoral research assistant at the Robotics and Perception Group (RPG) in University of Zurich directed by Prof. Davide Scaramuzza. Currently, he is working on control and perception for aerial robots (drones).

In December 2020, he received his PhD degree in Aerospace Engineering from the Control and Simulation Group of Delft University of Technology. His works on quadrotor fault-tolerant flight control have been featured by reputable media, such as IEEE Spectrum.

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During the Automate 2022 trade show on June 6-9 in Detroit, Southwest Research Institute is introducing new automation technology that allows industrial robots to visually classify work and autonomously perform tasks.

Unmanned and master/slave are two terms that are offensive to many in the community. Such terms may once have been accepted by society, but not any longer, and we are pleased to see many organizations starting to use alternative terms.

We call on you in 2022 to remove words with negative connotations, like the ones listed below, from all materials, course descriptions, department names, products, forms, reports or articles. The benefit to you is in broadening your appeal to all the community members who find those terms, if not outrightly offensive, then at the least old-fashioned and representative of a mindset that has not engaged meaningfully with creating inclusive or modern robotics.

Women in Robotics has taken the lead in curating a list of best practices in inclusive terminology, in consultation with other groups, and now we would like to share the first draft of “Terminology for 21st Century Technologists” for comment. So far we’ve considered gender, ethnicity and some disability issues. Our goal is to create a comprehensive directory of terminology, which can go through an update process periodically, just as standards do. There is obviously a little more work to be done and we want to include sections on ‘how to retire terms’, ‘how to implement changes constructively’ and more information about the process. This is where you can help us.

Please send comments in response to the first draft by June 28 2022 to reports@womeninrobotics.org