Daily Archives: June 15, 2024

Robots operating autonomously could soon become valuable data sources for industrial companies, providing insights and repeated measurements that humans struggle to match, according to the director of the Oxford Robotics Institute.

Speaking at the AI Summit London, Nick Hawes explained how his team has deployed autonomous robots like Boston Dynamics' robot dog Spot at sites like a former nuclear fusion reactor, gathering radiation data over 35 days with minimal human involvement.

The Oxford Robotics Institute consists of seven different research groups working across the robotics stack on tasks including dexterous control manipulators to fully AI-driven robots in industrial settings.

He said 80% of the Institutes work relates to AI both in a general setting working on fundamental research questions and in the client setting.

The Institutes early work in robotics led to the research that put the first autonomous cars on the road through Oxbotica, now known as Oxa.

Now theyre working on autonomy, specifically, deploying robots in industrial settings, getting robots into places humans cant reach.

Hawes said industrial companies are considering autonomy, but they largely prefer a human-in-the-loop approach, where operators collaborate with robots and can take control when necessary.

Related:Vodafone AI Expert Highlights Key Factors for Effective Business Chatbots

The move toward full autonomy wont occur overnight, Hawes explained, as systems need to get better at handling the uncertainty.

If the world was fixed and 100% predictable then wed write a Python script and be done, Hawes said. The reason you need AI on a robot is because it needs to respond to changes.

He outlined several autonomous robotic deployments his team has been involved in, including using quadrupedal robots from Boston Dynamics to patrol industrial plants.

For example, his team fitted Spot robots with lidar and advanced 3D mapping technologies so they could reliably navigate an industrial site autonomously as well as a mission or task-specific payload, like a hardware device for monitoring radiation levels, for example.

Oxford Robotics Institute showcased Boston Dynamic's Spot at AI Summit London | Ben Wodecki

In another deployment, Spot operated autonomously for 35 days, walking around the U.K.s former fusion reactor site. It was tasked with gathering data on alpha radiation emissions and Hawes said it required minimal involvement, programmed to return to its charging unit when its battery was low.

The robot had a little script that said, here are the six places I want you to look at and this is what your battery looks like, Hawes said. The robot was able to then plan and optimize for that information to get up every day, walk around the site, gather the information and go back home.

Related:Quantum, AI Combine to Transform Energy Generation, AI Summit London

With robots operating for long periods, theyre going to create multiple maps of the environment something Hawes said could prove helpful for operators as they can compare the findings.

The 35-day deployment created repeatable data that can be fed into a different solution, like a digital twin to create virtual representations of industrial environments, for example.

A robot is something that can deliver actionable data over long periods of time, Hawes said. Humans struggle because they get bored and point the camera at the wrong place or forget to take an image whereas a robot gives you a bit more reliability.

Hawes showed how the data from the fusion reactor site could be used to create a virtual environment emergency services could use to understand the environment before entering the building in case of a disaster.

Describing the concept of artificial general intelligence (AGI) as nonsense the professor said robotics developers like his team were focused on autonomy that flexibly changes what that robot performs.

Instead of AGI, he described autonomy as a robot having the ability to repeatedly perform tasks or measure data in the same way, every time.

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Robots as a Data Source: Obtaining Insights From Robotic Autonomy - AI Business

David Grossman is arguably one of Israels greatest living writers. He wrote a wrenching New York Times op-ed on March 1 titled Israel Is Falling into an Abyss. In it, he wrote about the fear, the shock, the fury, the grief and humiliation and vengefulness, all flowing into the open wound of October 7.

A funny thing happened to me on my way to writing a more hopeful response. I encountered some of the very best of Israel its youth and discovered firsthand not an abyss into which Israel is falling but a steep challenging mountain that young Israelis are ascending with energy, courage, and brilliance. I spoke with some of the youth who will build and rebuild our countrys future.

On April 18, a group of young Israelis from Cramim Binyamina High School boarded a British Airways plane on their way to Houston, Texas. It was only four days after Iran sent some 300 drones, cruise missiles, and ballistic rockets at Israel, at midnight Saturday, between April 13 and 14, 190 days after October 7.

With many airlines canceling flights, it was not certain they and other participants would make it to Houston. A few never did.

The group sophomores, juniors, and seniors were headed to Texas to compete in the global four-day FIRST robotics competition. With them came their little robots. In all, some 550 youth and adults comprising 14 Israeli teams traveled to Houston, competing in several events.

A few days later, the Orbit 1690 team from Cramim was on its way home as world champions. Their team built robots that outclassed the field. Israeli youth Israel: population almost 10 million had competed with, and cooperated with, teams from the US (population 330 million) and China (1.4 billion). And emerged champions. (See box: Winning team members).

I spoke with Mika Elias, a team member specializing in software code at the Technion. A high school senior, she was part of a group of 31 young people completing their high school education, and this fall about to start a year of National Service, mentoring young people in FIRST robotics and encouraging them to study STEM (science, technology, engineering, and math).

From Elias, I learned the inspiring story of how this talented, creative, and determined handful of young people from a high school in Binyamina won the Super Bowl of robotic competitions.

Full disclosure: Eliass grandfather is my Technion colleague, emeritus professor Ezra Elias, mechanical engineering; and her grandmother is Noa Elias, a school psychologist and longtime friend and colleague of my wife, Dr. Sharone Maital.

Elias said that her grandfather helps her study physics and math, and her grandmother offers wise counsel.

Values come FIRST: FIRST is an international youth organization. The acronym stands for For inspiration and recognition of science and technology. It was founded by Dean Kamen in 1989.

Kamen is best known as the genius inventor of the Segway, a two-wheeled, self-balancing personal vehicle. Born to a Jewish family in Long Island, New York, he showed early talent as an entrepreneur. In high school, he was already earning $60,000 a year building light and sound systems for local musical bands.

Kamen was disappointed with the paucity of young people worldwide who chose science and technology careers dj vu for Israel. Kamen partnered with legendary MIT professor Woodie Flowers, who developed a design competition for his famous MIT course 2.007 Design and Manufacturing. It became an annual MIT event for over five decades and is widely emulated around the world. Kamen says of all his innovations, he is most proud of FIRST.

From the first competition in 1992, FIRST has grown to include over 3,000 high school teams from some 60 countries. Unlike cutthroat team sport competitions, FIRST imbues Flowers gracious professionalism, built on empathy and respect for other teams, structured around coopetition in which teams learn equally to compete and to collaborate.

Incoming FIRST Israel CEO Gila Kusan told The Jerusalem Posts reporter Eyal Green that there is intensive work on the core values, to give the kids a sense of security. Throughout the teams preparation, there is a feeling of camaraderie we are the FIRST family; despite that horrific [October 7] event, we found a way to have this activity, and it allowed us to attain incredible achievements.

Elias enumerated to me the core values she learned in the FIRST program: inclusiveness, fun, innovation, discovery, teamwork, impact on and service to the community. In her National Service year, she will mentor young people in their FIRST teams, including those evacuated from the Gaza and northern borders, to help instill those values. She will then join the IDF and spend six years writing software code.

How the competition works: Elias explained that it all began in early January with a broadcast on Twitch, an American live-streaming service, explaining the mission to design robots that pick up disks and toss them high into a bin. High school teams all over the world receive an instruction manual, defining the specifications, and a set of parts to which they can add off-the-shelf. (Next years competition will apparently involve underwater robots).

Teams organize and choose management leaders and specialists in mechanics, software, and CAD (computer-aided design). They plan, build, test, and code leading up to the late April global contest in the US.

Defense, defense: I asked Elias why she thought the Orbit 1690 team from Cramim won. She lauded the teams robot driver, Yoav Rahmani.

Hes a terrific driver! she said. Robots are partly autonomous, driven by software, but partly non-autonomous, with drivers who operate them.

Like soccer teams, robots attack, to score points by flipping disks into bins; and defend, to prevent opponent robots from scoring.

In the championship final contest, four Blue teams, including Israels Orbit 1690, battled four Red teams. The Blue squad chose a kind of Iron Dome strategy, defending aggressively to keep the opponent from scoring. It worked. The Blue squad won five of six bouts in the final.

There is a powerful lesson here. Part of FIRSTs value-based culture is focused on fostering collaboration. In the final, Orbit 1690 had to collaborate with three other teams in a very short time teams they were unfamiliar with. It was rather like Argentinas soccer team merging with rivals Brazil in the World Cup, seamlessly integrating the two teams, and then competing with a merged France-England team, all in just a day or two.

For the record, Israels team joined with Team SCREAM from Sedalia, Missouri; Team 8-Bit from Phoenix, Arizona; and Team RoboLancers from Philadelphia, Pennsylvania, to win the championship.

The Shadow of October 7: Elias and her fellow team members worked intensively on their robots in the shadow of the October 7 trauma.

FIRST Israel CEO Ido Mazursky was called up for IDF reserve duty for the first three weeks of the war. On his return, he gathered the FIRST mentors and told them to get the kids to the workshops. He told The Jerusalem Post that this years preparation and competition were dedicated to five FIRST alumni who fell in Gaza and the southern border: Itai Seif; Joseph Yosef Gitarts; Ohad Ashur; Jonathan Mimon; and Marguerite Gosak.

Kudos to the school: Cramim Binyamina is a six-year state high school, grades 7-12, with about 1,700 students and 170 teachers and administrative staff. The schools website asserts, We are a school that combines science and technology studies, together with social and humanistic studies ...We provide personal treatment to each of our students in a warm inclusive environment, while providing a large variety of social options. The schools professional team educates our students in values, professionalism, and excellence and encourages them to develop their abilities.

Well done, Cramim! Israels Education Ministry is criticized almost daily for its failings. Lets recognize that there are many terrific schools, with equally terrific kids and inspired teachers, who will lead Israels hi-tech economy to new heights.

The Cramim team was not the only Israeli winner. A team from a Holon high school excelled, too. They were finalists for the coveted Impact award for community service with youth evacuated from border areas.

Jewish values: In last years global FIRST event, an Israeli high school robotics team from a school in Modiin, the national champion, withdrew from competition the day before the finals. They explained in a letter that because the finals were scheduled for Shabbat, they could not compete. Their letter was read over the loudspeakers and drew a standing ovation from the crowd.

What makes a champion? I was eager to learn Israels secret sauce. What is it about our youth that leads them to excel in robot competitions, in defending our country, and later, in building hi-tech start-ups that drive our economy?

Elias recounted her family history. As a young child, she spent six years in the US, when her fathers start-up Coral Sense operated there. Then she lived for 18 months with her family on a catamaran (double-hulled boat), sailing in the Caribbean and being home-schooled, when she was eight and nine. Later, she attended Amirim Elementary School in Binyamina; her classmates were among those who later became her FIRST teammates at Cramim. Her father taught her software coding, a skill which she later expanded through study, mentoring, and courses.

The secret sauce? We have flocks of visitors from abroad to the Technion, eager to learn the recipe. I tell them candidly that I dont know what it is. Perhaps our peoples 3,500 years of struggle, survival, resilience, and creative thinking. And our Torah, which commands us to strive to be a blessing to the world.

An abyss is a seemingly bottomless pit. The word abyss is also used to describe an unbridgeable gap between competing ideologies or policies.

Yes, David Grossman, it did seem for a time that Israel was indeed creating and falling into an abyss, long before October 7. At times, it still seems so. But since 1967, I have had the privilege, nearly every single workday, to interact with bright young people.

I wish my readers could have joined me, sitting opposite Mika Elias, to bask in the boundless energy, optimism, and hopefulness she radiated. Her eyes sparkled as she recounted her adventure, describing how she and the Cramim team planned, built, and operated those amazing little robots.

It cannot be denied. Our young people are capable of amazing feats. And they will lead us toward a better, brighter future. Count on it.

For the record, these are the Cramim School robotics team members that brought home the world championship: Management Team: Hadar Bar Aharoni, team captain & CAD lead; Ella Lidor, team captain; Software Team: Omer Prag, software lead robot; Amit Chayko, Mika Elias; Omri Lerner, software lead vision; Itamar Schwartz, software lead apps; Itay Nauman, Michal Landwer, Amit Askof, Dor Inon, Ido Zipori, Doron Malka, Yan Vazan, Ori Krisi, Yahav Fruchter; CAD Team: Noam Tal, Jonathan Shaharabani, Bat Chen Shaked, Alon Gonen, Aviv Rozen, Ella Nuriel, Shai Nisenbaum, Yonatan Harel; Yoav Rahmani, Yotam Manash; Mechanics Team: Tamir Sivan, Mechanics lead; Raz Peretz, Mechanics lead; Eitan Katzir, Dana Nisim, Noga Shubinsky, Gabrielle Garih; Danny Bryskin; Jonathan Musli, Tomer Harduff.

The writer heads the Zvi Griliches Research Data Center at S. Neaman Institute, Technion. He blogs at http://www.timnovate.wordpress.com.

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FIRST robotics victory is a win and hope for Israeli education - The Jerusalem Post

Humanoid robots are one of those dreams that sometimes feel like were on the precipice of realizing. Boston Dynamics has its Atlas robot, and Tesla is pursuing robotics, while companies like Mercedes, Amazon, and BMW are or will be testing robots for industrial use. But those are all very expensive robots performing tasks in controlled environments. In the home, they might still be far off.

Enter Apple. Mark Gurman at Bloomberg has said its robotics projects are under the purview of former Google employee John Giannandrea, who has been in charge of Siri and, for a time, the Apple Car. With the car project canceled, the Vision Pro launched, and Apple Intelligence around the corner, is that the next big thing?

According to his information, any humanoid Apple robot is at least a decade away. Still, simpler ideas may be closer a smaller robot that might follow you around or another idea involving a large iPad display on a robotic arm that emotes along with the caller on the other end with head nods and the like.

Many, if not most, homes are dens of robot-confounding chaos

A mobile robot is tricky, though; what in the world would Apple do with a home robot that follows me around? Will it play music? Will it have wheels, or will it walk? Will I be expected to talk to Ajax or SiriGPT or whatever the company names its chatbot? Or, given Apples rumored OpenAI deal, some other chatbot?

For that matter, what form will it take? Will it fly? Will it have wheels? Will it be a ball? Can I kick it?

Its form factor will be at least as important as its smarts. Houses have stairs, furniture that sometimes moves, clothes that end up on the floor, pets that get in the way, and kids who leave their stuff everywhere. Doors that opened or closed just fine yesterday dont do so today because it rained. A haphazard kitchen remodel 20 years ago might mean your refrigerator door slams into the corner of the wall by the stairs because why would you put the refrigerator space anywhere else, Dave? But I digress.

Based on what little detail has trickled out, Apples robotics ideas seem to fit a trend of charming novelty bots weve seen lately.

One recent example is Samsungs Bot Handy concept, which looks like a robot vacuum with a stalk on top and a single articulating arm, meant to carry out tasks like picking up after you or sorting your dishes. Theres also the cute ball bot named Ballie that Samsung has shown off at a couple of CES shows. The latest iteration follows its humans and packs a projector that can be used for movies, video calls, or entertaining the family dog.

Meanwhile, Amazons $1,600 home robot with a tablet for a face, Astro, is still available by invitation only. It is charming, in a late 90s Compaq-computer-chic aesthetic sort of way, but its not clear that its functionally more useful than a few cheap wired cameras and an Echo Dot.

LG says its Q9 AI Agent is a roving smart home controller that can guess your mood and play music for you based on how it supposes youre feeling. Im very skeptical of all of that, but it has a handle, and I do love a piece of technology with a built-in handle.

I still want a sci-fi future filled with robotic home assistants that save us from the mundane tasks that keep us from the fun stuff we would rather do. But we dont all live in the pristine, orderly abode featured in Samsungs Ballie video or the videos Apple produces showing its hardware in personal spaces. Many normal homes are dens of robot-confounding chaos that tech companies will have a hard time accounting for when they create robots designed to follow us or autonomously carry out chores.

There are other paths to take. Take the Ring Always Home Cam, which will be very noisy judging from the demo videos, but it could also be useful and even good. While putting aside the not insignificant privacy implications for a moment, it seems promising to me mostly because of the mobility and that its only designed to be a patrolling security camera.

That kind of focused functionality means its predictable, which is what makes single-purpose gizmos and doodads work. After some experimentation, my smart speakers are where they hear me consistently or are the most useful, and I can put my robot vacuums in the rooms I know Ill keep clean enough that they wont get trapped or break something (usually).

The robot vacuums I have the Eufy Robovac L35 and a Roomba j7 do an okay job, but they sometimes need rescuing when they find my cats stringy toys or eat a paperclip (which are somehow always on the floor even though I never, ever actually need one or even know where we keep them).

I have a kid, see, and preparing the way for them in other parts of the house is just adding more work to the mix. Thats fine for me because the two rooms in their charge are the ones that need vacuuming the most, so theyre still solving a problem, but it waves at the broader hurdles robotic products face.

And its not all that clear that AI can solve those problems. A New York Times opinion piece recently pointed out that despite all the hand-wringing about the tech over the last year and a half, generative AI hasnt proven that it will be any better at making text, images, and music than the mediocre vacuum robot that does a passable job.

Given the generative AI boom and rumors that Apple is working on a HomePod with a screen, a cheerful, stationary smart display that obsequiously turns its screen to face me all the time seems at least vaguely within the companys wheelhouse. Moving inside the house and interacting with objects is a trickier problem, but companies like Google and Toyota have seen success using generative AI training approaches for robots that learn how to do things like make breakfast or quickly sort items with little to no explicit programming.

Itll be years, maybe even decades, before Apple or anyone else can bring us anything more than clumsy, half-useful robots that blunder through our homes, being weird, frustrating, or broken. Heck, phone companies havent even figured out how to make notifications anything but the bane of our collective existence. Theyve got their work cut out for them with homes like mine, where were just one busy week away from piles of clutter gathering like snowdrifts, ready to ruin some poor robots day.

See more here:

Apple's next nebulous idea: smart home robots - The Verge

By Alex Shipps | MIT CSAIL | MIT News

When robots come across unfamiliar objects, they struggle to account for a simple truth: Appearances arent everything. They may attempt to grasp a block, only to find out its aliteral piece of cake. The misleading appearance of that object could lead the robot to miscalculate physical properties like the objects weight and center of mass, using the wrong grasp and applying more force than needed.

To see through this illusion, MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers designed theGrasping Neural Process, a predictive physics model capable of inferring these hidden traits in real time for more intelligent robotic grasping. Based on limited interaction data, their deep-learning system can assist robots in domains like warehouses and households at a fraction of the computational cost of previous algorithmic and statistical models.

The Grasping Neural Process is trained to infer invisible physical properties from a history of attempted grasps, and uses the inferred properties to guess which grasps would work well in the future. Prior models often only identified robot grasps from visual data alone.

Typically, methods that infer physical properties build on traditional statistical methods that require many known grasps and a great amount of computation time to work well. The Grasping Neural Process enables these machines to execute good grasps more efficiently by using far less interaction data and finishes its computation in less than a tenth of a second, as opposed seconds (or minutes) required by traditional methods.

The researchers note that the Grasping Neural Process thrives in unstructured environments like homes and warehouses, since both house a plethora of unpredictable objects. For example, a robot powered by the MIT model could quickly learn how to handle tightly packed boxes with different food quantities without seeing the inside of the box, and then place them where needed. At a fulfillment center, objects with different physical properties and geometries would be placed in the corresponding box to be shipped out to customers.

Trained on 1,000 unique geometries and 5,000 objects, the Grasping Neural Process achieved stable grasps in simulation for novel 3D objects generated in the ShapeNet repository. Then, the CSAIL-led group tested their model in the physical world via two weighted blocks, where their work outperformed a baseline that only considered object geometries. Limited to 10 experimental grasps beforehand, the robotic arm successfully picked up the boxes on 18 and 19 out of 20 attempts apiece, while the machine only yielded eight and 15 stable grasps when unprepared.

While less theatrical than an actor, robots that complete inference tasks also have a three-part act to follow: training, adaptation, and testing. During the training step, robots practice on a fixed set of objects and learn how to infer physical properties from a history of successful (or unsuccessful) grasps. The new CSAIL model amortizes the inference of the objects physics, meaning it trains a neural network to learn to predict the output of an otherwise expensive statistical algorithm. Only a single pass through a neural network with limited interaction data is needed to simulate and predict which grasps work best on different objects.

Then, the robot is introduced to an unfamiliar object during the adaptation phase. During this step, the Grasping Neural Process helps a robot experiment and update its position accordingly, understanding which grips would work best. This tinkering phase prepares the machine for the final step: testing, where the robot formally executes a task on an item with a new understanding of its properties.

As an engineer, its unwise to assume a robot knows all the necessary information it needs to grasp successfully, says lead author Michael Noseworthy, an MIT PhD student in electrical engineering and computer science (EECS) and CSAIL affiliate. Without humans labeling the properties of an object, robots have traditionally needed to use a costly inference process. According to fellow lead author, EECS PhD student, and CSAIL affiliate Seiji Shaw, their Grasping Neural Process could be a streamlined alternative: Our model helps robots do this much more efficiently, enabling the robot to imagine which grasps will inform the best result.

To get robots out of controlled spaces like the lab or warehouse and into the real world, they must be better at dealing with the unknown and less likely to fail at the slightest variation from their programming. This work is a critical step toward realizing the full transformative potential of robotics, says Chad Kessens, an autonomous robotics researcher at the U.S. Armys DEVCOM Army Research Laboratory, which sponsored the work.

While their model can help a robot infer hidden static properties efficiently, the researchers would like to augment the system to adjust grasps in real time for multiple tasks and objects with dynamic traits. They envision their work eventually assisting with several tasks in a long-horizon plan, like picking up a carrot and chopping it. Moreover, their model could adapt to changes in mass distributions in less static objects, like when you fill up an empty bottle.

Joining the researchers on the paper is Nicholas Roy, MIT professor of aeronautics and astronautics and CSAIL member, who is a senior author. The group recentlypresented this workat the IEEE International Conference on Robotics and Automation.

Reprinted with permission of MIT News

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Helping Robots Grasp the Unpredictable - The Good Men Project

Tesla has fully restarted its hiring effort beyond AI and robotics in a big way after a hiring freeze amid several waves of layoffs throughout the entire organization.

As we previously reported, Elon Musk came into Tesla like a wrecking ball earlier this quarter and fired an estimated 15-20% of Teslas staff.

Sources said Musk had greatly reduced his involvement at Tesla over the last year, but the CEO reasserted himself amid a proxy battle over his CEO compensation plan that was rescinded by a judge earlier this year.

Musk fired many of Teslas top executives, and others left.

With these layoffs, Tesla effectively put in place a hiring freeze.

A few weeks ago, we reported on Tesla restarting to hire, but only for the AI and robotics department.

The hiring freeze now seems to be officially over in the US as Tesla has posted hundreds of new positions across several departments but mainly service and sales:

Teslas layoffs have affected its service, sales, and delivery departments despite being at capacity in many regions.

As we previously reported, this greatly affected morale as Tesla workers not only lost friends, but they also saw their already heavy workload increase.

In some cases, Tesla is expected to rehire some of the employees it has let go, which has been the case after previous rounds of layoffs and, more recently, after Musk fired Teslas entire charging team.

On top of service and sale jobs, the automaker also posted several new positions at its lithium refinery under construction in Robstown, Texas.

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Tesla restarts hiring beyond AI and robotics in a big way - Electrek.co

Tesla appears to be behind the acquisition, or acqui-hire, of a company developing direct drive motors for robots as it is being liquidated by Koch Engineered Solutions.

Besides the SolarCity acquisition, Tesla has avoided large acquisitions despite a significant cash position.

However, the automaker is known to have made several smaller acquisitions, especially in the manufacturing industry, either to secure manufacturing automation technologies or to acqui-hire, which refers to acquiring a companys talent.

Here are several examples:

Now, Electrek has learned that Tesla might be adding a company, or at the very least its talent, to that list.

Genesis Motion Solutions is an engineering firm specializing in direct drive motors based in British Colombia, Canada.

In 2018, it received a strategic, controlling investment from fossil fuel giant Koch Industries.

Earlier this year, they announced that Koch was stopping operations of the company and liquidating it:

Shortly after, Electrek noted that many Genesis engineers started to join Tesla. Now, Electrek has found 18 former employees of Genesis who have joined Tesla.

The first engineers to joined Tesla from Genesis were Matt Balisky and Nick di Lello last year prior to Koch liquidating the company. Their role of design actuators for humanoid robotics at Tesla hints at the companys interest in Genesis.

Genesis main product was LiveDrive. The company described the product on its website before taking it down last year:

Introducing LiveDrivehoused and frameless direct drive rotary motors engineered with patented electromagnetic technology for more torque to mass than competing direct drive motors, resulting in maximum productivity and efficiency for your machinery.

Direct drive motors offer highly dynamic acceleration and a high level of positional precision,often resulting in high efficiency.

Their main downsides are generally their costs and their limited torque.

It makes them interesting solutions as actuators for robots, which is one of the applications envisioned by Genesis founder and LiveDrive inventor James Klassen

In the latest generation of its Optimus robot, Tesla has noted that it started to incorporate its own actuators designed in-house.

We couldnt confirm if Tesla is only acquiring Genesis talent amid the liquidation, much like an acqui-hire situation, or if the automaker is acquiring some or all of the companys assets.

Electrek checked the companys Canadian patents and Genesis is still the owners on record.

Tesla recently announced that it deployed its first two Optimus robots inside its factories and it plans to sell them to customers as soon as next year.

Interestingly, most of the new hires from Genesis came amid the big wave of layoffs earlier this year.

However, Teslas AI and Robotics department, which is leading the development of Teslas self-driving effort and Optimus humanoid robot, has been one of the rare departments spared in the round of layoffs.

Elon is making it clear that Teslas priority is self-driving and humanoid robot. After laying off as much as 20% of the staff, its clear that those are the safest jobs at Tesla.

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Tesla jumps on talent of Koch company making direct drive for robots - Electrek.co

Most coverage of humanoid robotics has understandably focused on hardware design. Given the frequency with which their developers toss around the phrase general purpose humanoids, more attention ought to be paid to the first bit. After decades of single-purpose systems, the jump to more generalized systems will be a big one. Were just not there yet.

The push to produce a robotic intelligence that can fully leverage the wide breadth of movements opened up by bipedal humanoid design has been a key topic for researchers. The use of generative AI in robotics has been a white-hot subject recently, as well. New research out of MIT points to how the latter might profoundly affect the former.

One of the biggest challenges on the road to general-purpose systems is training. We have a solid grasp on best practices for training humans how to do different jobs. The approaches to robotics, while promising, are fragmented. There are a lot of promising methods, including reinforcement and imitation learning, but future solutions will likely involve combinations of these methods, augmented by generative AI models.

One of the prime use cases suggested by the MIT team is the ability to collate relevant information from these small, task-specific datasets. The method has been dubbed policy composition (PoCo). Tasks include useful robot actions like pounding in a nail and flipping things with a spatula.

[Researchers] train a separate diffusion model to learn a strategy, or policy, for completing one task using one specific dataset, the school notes. Then they combine the policies learned by the diffusion models into a general policy that enables a robot to perform multiple tasks in various settings.

Per MIT, the incorporation of diffusion models improved task performance by 20%. That includes the ability to execute tasks that require multiple tools, as well as learning/adapting to unfamiliar tasks. The system is able to combine pertinent information from different datasets into a chain of actions required to execute a task.

One of the benefits of this approach is that we can combine policies to get the best of both worlds, says the papers lead author, Lirui Wang. For instance, a policy trained on real-world data might be able to achieve more dexterity, while a policy trained on simulation might be able to achieve more generalization.

The goal of this specific work is the creation of intelligence systems that allow robots to swap different tools to perform different tasks. The proliferation of multi-purpose systems would take the industry a step closer to general-purpose dream.

Excerpt from:

Generative AI takes robots a step closer to general purpose - TechCrunch

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The Robotics Manufacturing Hub is modular, adaptable, and multi-use, with OEM diversity. Source: The ARM Institute

When the ARM Institute launched its Robotics Manufacturing Hub about a year ago, it quickly realized that U.S. manufacturers werent looking at robotics and automation because they werent interested in the technology. Instead, the barriers to automation loomed so large that it was impossible for small and midsize firms to know where to start.

When the ARM Institute announced its no-cost Robotics Manufacturing Hub for manufacturers in the Pittsburgh region, its pipeline of interested manufacturers rapidly filled. With the ARM Institute offering a pathway to minimize the risks they associate with robotics and automation, U.S. manufacturers were, and still are, eager to explore the possibilities.

Larger manufacturing firms can more easily navigate the process of implementing automation. With greater general resources, in-house R&D, financing to invest in the upfront costs, and more time to explore solutions, theyve more successfully been able to see the process through from start to finish.

Small and midsize manufacturers (SMMs) have to navigate more risk. They need to spend more time understanding how the changes will affect their operations. They often lack in-house robotics expertise, and they need systems that will dynamically meet their needs without requiring constant upkeep when, in many cases, their workforce is already strained.

The ARM InstitutesRobotics Manufacturing Hubis a free resource to help manufacturers navigate these barriers and others by identifying the best business cases for robotics, testing the systems within the manufacturers budget, and offering a path to implementation. Part of this solution includes the ability for SMMs in Southwestern Pennsylvania to work directly with the institutes team of robotics engineers and get hands-on with advanced technologies in the institutes Pittsburgh facility.

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Since the Robotics Manufacturing Hubs creation, the ARM Institute has worked with several manufacturers in the Pittsburgh region to explore their challenges and help them understand where robotics can address these challenges.

For example, the ARM Institute worked with a manufacturer of castings and forgings to automate its manual quality-inspection process. Partnering with FARO and NEFF Automation through the Robotics Manufacturing Hub, the ARM Institute performed a proof-of-concept of a Universal Robots cobot controlling a FARO laser scanner. The manufacturer plans to pursue implementation.

The ARM Institute also worked with a company that needed to package heavy iron and steel parts into shipping containers, creating an ergonomically uncomfortable task for a human worker. In this situation, requirements for the robotic end effector were highly specific, and its critical to calculate the correct pick place on the parts and speed limitations of the robot to move heavy parts and prevent failure or injury.

The ARM Institute is working with its member CapSen Robotics on a solution.

CapSen Robotics has designed end effectors to sort metal parts. Source: CapSen Robotics

Much of this work is completed using the ARM Institutes headquarters as a neutral ground for exploration and prototyping, giving manufacturers access to equipment before they commit to installing any system.

This facility is modular, adaptable, and multi-use, with OEM diversity to directly meet each manufacturers individual needs. ARM Institute engineers work directly in the lab and interface between suppliers and manufacturers to act in the SMMs best interest and ensure that the work will address the specific challenges the company is facing.

Below is a brief overview of the equipment available through the Robotics Manufacturing Hub and application areas that can be addressed using this equipment:

The cobots can be configured for the following applications:

The industrial robots can be configured for the following applications

Small and midsize manufacturers in the Pittsburgh region can get a free automation assessment and use the Robotics Manufacturing Hub at no cost, thanks to funding from the Southwestern Pennsylvania Regions Build Back Better Regional Challenge Award. Now is a great time to get started with the hub, as the ARM Institute is looking to work with more manufacturers.

In the future, the ARM Institute hopes to expand these services to manufacturers beyond this region and encourages those with interest in using or housing these services to reach out. In addition, the ARM Institutes member ecosystem can use the Robotics Manufacturing Hub as a benefit of membership.

According to the ARM Institutes Future of Work study released last week, industry trends include keeping people in the loop and the need for organizations to learn how to use data as artificial intelligence increases in importance. As a result, the institute noted that manufacturers and training centers must develop programs to help workers develop the skills needed to stay competitive and adapt to new technologies.

U.S. manufacturing resiliency is the cornerstone of our national security. The ARM Institutes Robotics Manufacturing Hub addresses a critical need in helping to provide SMMs with the resources that they need to explore and implement automation, enhancing their competitiveness and benefiting the full manufacturing ecosystem.

Larry Sweet last year became director of engineering at the Advanced Robotics for Manufacturing (ARM) Institute in Pittsburgh. He has experience in bringing emerging technologies into production by increasing their Technology Readiness Level, concurrent with improvements in factory floor processes and workforce skills.

Sweet was previously the director for worldwide robotics deployment at Amazon Robotics, leading technology transition and system integration for all internally developed automation into Amazons global network. He has also held senior manufacturing and technology roles at Symbotic, the Frito-Lay, United Technologies, ABB, FANUC, and GE. Sweet spoke at the 2024 Robotics Summit & Expo in May.

Editors note: This article is syndicated fromThe Robot Reportsibling siteEngineering.com.

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Robotics Manufacturing Hub to help small and midsize U.S. manufacturers compete - Robot Report

A U.K. robotics startup has claimed its new robot hand designed for artificial intelligence (AI) research is the most dexterous and robust out there.

The Shadow Robot Companys "Shadow Hand," built in collaboration with Googles DeepMind, can go from fully open to closed within 0.5 seconds and can perform a normal fingertip pinch with up to 10 newtons of force.

Its primarily built for AI research, specifically "real-world" machine learning projects that focus on robotic dexterity. These projects may include TK EXAMPLE (OpenAI is using a Shadow Hand device for dexterity training, teaching it to manipulate objects in its hand). However, the Shadow Hand's durability is its key selling point, with the device able to endure extreme punishment, such as aggressive force and impacts.

"One of the goals with this has been to make something that is reliable enough to do long experiments," Rich Walker, one of Shadow Robots directors, said May 30 in a blog post. "If youre doing a training run on a giant machine learning system and that run costs $10 million, stopping halfway through because a $10k component has failed isnt ideal.

"Initially we said that we could try and improve the robustness of our current hardware. Or, we can go back to the drawing board and figure out what would make it possible to do the learning you need. Whats an enabling approach here?"

Related: Robot hand exceptionally 'human-like' thanks to new 3D printing technique

What exactly makes the Shadow Hand so robust isnt entirely clear: the company website states only that it is "resistant against repeated impacts from its environment and aggressive use from an untrained policy," which does little to explain the methods and materials used. But in his blog post, Walker suggested trial and error was the key to the sturdiness of the robotic hand.

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"We spent a huge amount of time and effort testing the various components, iterating the design, trying various things," Walker explained."It was a very integrated project in terms of collaboration and iterative development. The end result is something quite special. Its not a traditional robot by any means."

The Shadow Robot Company previously demonstrated an earlier robot hand at Amazon re: MARS. Shadow Hand, however, is its latest model. It has been built with precise torque control and each of its fingers is driven by motors at their base and connected via artificial tendons.

Each finger is a self-contained unit with sensors and stereo cameras simulating a sense of touch. The segments that make up the fingers are fitted with tactile sensors, and a stereo camera setup provides high-resolution, wide-dynamic-range feedback. The cameras are specifically pointed towards the inside of the surface of the silicon-covered fingertips so that they can capture the moment it touches something and convert this visual data into other types of data.

Should any of the appendages endure significant damage, they can simply be removed from the base model and replaced. The sensors can also be replaced if need be, with the internal network able to identify when a sensor has been removed and a new one added.

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DeepMind experimenting with 'Shadow Hand' that can withstand a severe beating in the name of AI research - Livescience.com

In a move thats set to reshape the landscape of robotics, Collaborative Robotics unveiled its ambitious plans for the future this week. The company, known for its innovative collaborative robots (cobots), is stepping into the forefront of AI with the formation of a dedicated Foundation Models AI team. This powerhouse team, led by Michael Vogelsong, a veteran of Amazons Deep Learning Tech team, will be based in a new Seattle office, further solidifying the citys reputation as a tech hub.

Our cobots are already doing meaningful work in production on behalf of our customers, said Brad Porter, CEO of Collaborative Robotics in an article by The Robot Report. Our investment in building a dedicated foundation models AI team for robotics represents a significant step forward as we continue to increase the collaborative potential of our cobots.

The focus of this new AI team is clear: to explore the cutting edge of AI in enhancing robotic capabilities, especially in areas like bimanual manipulation and low-latency multimodal models. Their goal is to create robots that can understand and respond to complex tasks and environments with a level of comprehension and control never before seen.

The company announced on its website that it has secured a new Seattle office and a research grant for University of Washington professor Sidd Srinivasa to support advanced AI research. Industry reports indicate that roughly 30 employees will be working at the companys new offices at 100 NE Northlake Way.

This strategic move follows a successful $100 million Series B funding round in April, which will be used to commercialize Collaborative Robotics autonomous mobile manipulator. Details about this innovative system remain tightly under wraps, but snippets of information reveal a wheeled collaborative robot with omnidirectional motion and the ability to handle totes and boxes in warehouse settings.

The opportunity surrounding foundation models AI in the robotics industry can be significant. These models hold the promise of generalizing behaviors and streamlining the development and maintenance of special-purpose models, the report stated. Collaborative Robotics is prioritizing work in this arena, integrating advanced machine learning techniques into its production robots. This approach, coupled with novel research and partnerships, looks to revolutionize the adaptability and precision of robotic tasks.

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Collaborative Robotics Opens Seattle Office After $100MM Raise - The Registry Seattle