Monthly Archives: May 2024

The Cesar Chavez Ravenswood Middle School robotics team in Dallas, Texas at the VEX World Championship in April 2024. Courtesy Ravenswood City School District. Ravenswood robotics team makes it to world championship

The Cesar Chavez Ravenswood Middle School robotics team students made the division finals at the VEX World Championship in Dallas, Texas at the end of April.

The best part of the trip is when they unveil the new game and I go to dinner with the students and listen to all the ideas flying around the table and they start drawing pictures on napkins, said teacher Tina Collier, who leads the team, in a Ravenswood City School District newsletter. I carry a small sketchbook now to allow for everyone to participate in their first unofficial brainstorming session.

Senior citizens who own property in the Las Lomitas School District have until June 3 to apply for an exemption from the $311 parcel tax passed by voters in March 2007.

To qualify for the exemption, the property owner must be over 65 years old by July 1, 2024 and live on the property they own within district boundaries.

Contact district administration at 650-854-6311 for the application form.

The Menlo Park Safe Routes to School Program, in collaboration with the city library, will hold a Bike to the Library Day on Thursday, May 16, from 2-5:30 p.m. at Menlo Park Central Library, 800 Alma St. May is National Bike Month.

There will be helmet giveaways, bike skills courses, reading sessions, pedal-powered spin art, a bike repair station, helmet fitting station, food from local vendors, a SamTrans bus and more.

Representatives from SamTrans, Caltrain, WheelKids and others will be onsite.

More information is available at menlopark.gov.

Angela Swartz

Sudden Oak Death is an exotic disease that threatens the survival of tanoak and several oak species in California coastal forests. The SOD Blitz Project will be surveying the trees of Woodside, Portola Valley and Los Altos Hills from May 17 at 10 a.m. through May 20 at 6 p.m.

SOD Blitz invites the community and local citizen scientists to get involved in detecting the disease on bay laurel and tanoak leaves by collecting samples. All participants are required to take a 30-minute training course. To sign up and participate, visit tinyurl.com/SODBlitzProject.

Jennifer Yoshikoshi

Grab some free, nutritious compost for your Garden at the City of Menlo Parks compost giveaway event all day Saturday, May 11, and Sunday, May 12. Free compost is available while supplies last.

You can find the compost in the Burgess Park parking lot at the intersection of Alma Street and Burgess Drive. Compost is self-serve, so bring your own containers, trowels and gloves.

This compost comes from your residential green organic carts at home and at work, according to a city of Menlo Park event listing. The food scraps collected from the green carts are sent to two commercial composting facilities where it goes through a process where it breaks down back into a soil-like product.

Learn more about housing resources at the city of Menlo Parks Housing Resource Fair on Saturday, May 11, from 1-4 p.m. The event will be held at the Ivy Plaza, located at 410 Ivy Drive in the outdoor space near the Belle Haven Child Development Center.

The event will offer information on affordable housing in Menlo Park, housing rehabilitation support, and more. Housing organizations at the event will include the Samaritan House, Project Sentinel, Habitat for Humanity Greater San Francisco, Rebuilding Together Peninsula and HIP Housing.

There will also be raffle prizes, a childrens activity table and snacks.

Eleanor Raab

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Community briefs: Ravenswood robotics at world championship, Bike to the Library Day - The Almanac Online

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JCHS Robotics shimes at VEX World Competition | Community News - The Tomahawk

Robotics & Automation INSIDER

While the adoption of autonomous mobile robots (AMRs) and collaborative robots (cobots) has increased in logistics and manufacturing industries, mobile manipulation robots (MMRs) or humanoid robots still seem like a distant reality. Manufacturing and logistics operations have some of the most advanced automated solutions, but many have been deployed in silos and have created islands of automation. MMRs provide an opportunity to address the remaining gaps between islands of automation and tackle new workflows that needed more flexible automation, believes Melonee Wise, Chief Product Officer at Agility Robotics (Corvallis, OR).

Wise presented her insights about MMRs at Automate 2024 during the session titled The Next Frontier of Automation: Human-Centric Robots, where she talked about how to prepare your facility for the next phase of robotics human-centric, humanoid robots and how to connect existing islands of automation.

Wise also shared several real-life use cases of how Agilitys humanoid robot named Digit can effectively handle typical payloads found in most logistics and manufacturing operations, particularly the manipulation, movement, and placement of totes and cases. Digit is suitable for these industries that are facing significant labor shortages, and are plagued by operations that are repetitive, taxing, and injury prone.

Agility is working on the use of reinforcement-learning and generative AI models to refine and enhance Digits capabilities, enabling it to acquire and hone useful skills over time. The integration of large language models (LLMs) for natural language commands allows for intuitive and effective human-robot interactions.

I think it becomes super compelling long term when we can build expert versions of LLMs or multimodal models that allow us to take all of the information all the things that we've done with any one robot and combine that into a larger database, that helps us optimize, said Wise. So instead of someone telling Digit to do a task through a LLM model, they could just prompt the model to say: I'm trying to solve this problem, can you tell me the best way to do this? and then it will, basically program Digit to do it, she added.

Its important to not forget about safety, said Wise. One of the next things we are going to see with MMRs is the real practical application for safety. Currently there is one R15.08 standard for governing industrial mobile robots that includes AMRs and MMRs. One of the big challenges of this standard, according to her, is that its somewhat silent on dynamically stable robots.

Here is an exclusive Tech Briefs interview with Wise, edited for length and clarity.

Tech Briefs: Do you see the adoption of humanoid robots in manufacturing and logistics industries growing? Which other applications will use MMRs?

Melonee Wise: I think what you'll see is a progression from industrial spaces to light industrial spaces to public spaces. Thats the kind of the approach we are taking. Industrial spaces are manufacturing, logistics, construction, and inspection. Light industrial spaces are things like back of house retail back of your grocery store or hotels and hospitals. Then general public is the front of the house and hotel, the front of the house grocery store in urban settings. We are very focused right now on being very good at logistics and manufacturing and tackling the problems in those spaces and then generalizing. We are trying to become the App Store for labor we have a standardized robot that you then deploy new capabilities to. So, imagine a world in which you eventually one day have your own home robot, and you want Digit to do a new task, so you just pull up your phone and you flick the new capability to Digit. Thats the grand vision we have but we see that the practical steps are to first start in spaces where we can be successful.

Tech Briefs: Are there any specific technological challenges that could prevent adoption of humanoid robots in industrial spaces?

Wise: The biggest barrier to entry to any of this is how do we make this technology safe? When you look at industrial environments or light industrial environments, all of that in the United States is regulated by OSHA. In the European market, it's regulated by different equivalent bodies for worker safety. And then when we get outside of these environments, it really comes down to just general public safety. That would most likely be governed by consumer safety laws in the United States . The biggest challenge is that right now we have a standard for industrial mobile robots, which covers MMRs, but it's somewhat silent on how you deal with robots like Digit. So, first thing that we have to do right now is help to work to improve that standard. And second, figure out a technology solution to engineer a safe solution.

Tech Briefs: Are there any enabling technologies in the humanoid robot space which are exciting right now?

Wise: There are a lot of exciting technologies that are related to the kind of large language models and foundation models that allow us to basically learn new behaviors and skills for the robot very rapidly. So that we can just train the robot and to learn policies for how to grab the tote in different ways or do new tasks like manipulate boxes versus totes or, or open doors. LLMs are very good at taking a high-level directive and then composing a set of tasks to be done. Right now, we have a visual programming language, but we hope in the near future, someone could just through natural language describe what they want the robot to do. Then it will compose the programming for the user behind the scenes and run that program.

Tech Briefs: Moving forward how do you see Digit evolving?

Wise: The big things that we are focused on in the next few years is full collaborative safety. This means being able to be side by side with people on the production floor and increasing some of the capabilities of Digit. Right now, its payload is 16 kgs and we want to go to 25 kgs. We want to be able to charge it faster and run for longer. I think thats the hardware focus. But when we think about the skills that we want Digit to have going forward, we are focused on it being able to pick up new skill sets that are beyond tote handling, box handling, and go to machine tending. Those are software advances.

Tech Briefs: When will humanoid robots become part of our lives?

Wise: I think it's important to reflect on how long it has taken for autonomous cars to become part of our daily lives because it's very similar in that nature. We started talking about autonomous cars being part of our daily lives in 2004. Twenty years later, we are still probably 10 years from that. In the next two years, MMRs will be part of everyday worker lives. Today, Agility is already deploying with customers. Over the next two to five years, you will see a large swath of adoption, very similar to AMRs. I think it will take a lot longer after that to go out to your home, but before that MMRs will get to construction and hospitals probably in the next 10 years.

This article was written by Chitra Sethi, Editorial Director, SAE Media Group. For more information, visit http://www.agilityrobotics.com.

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Humanoid Robots: The Next-Generation Robotic Workforce - Tech Briefs

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Large language models (LLMs) can accelerate the training of robotics systems in super-human ways, according to a new study by scientists at Nvidia, the University of Pennsylvania and the University of Texas, Austin.

The study introduces DrEureka, a technique that can automatically create reward functions and randomization distributions for robotics systems. DrEureka stands for Domain Randomization Eureka. DrEureka only requires a high-level description of the target task and is faster and more efficient than human-designed rewards in transferring learned policies from simulated environments to the real world.

The implications can be great for the fast-moving world of robotics, which has recently gotten a renewed boost from the advances in language and vision models.

When designing robotics models for new tasks, a policy is usually trained in a simulated environment and deployed to the real world. The difference between simulation and real-world environments, referred to as the sim-to-real gap, is one of the big challenges of any robotics system. Configuring and fine-tuning the policy for optimal performance usually requires a bit of back and forth between simulation and real-world environments.

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Recent works have shown that LLMs can combine their vast world knowledge and reasoning capabilities with the physics engines of virtual simulators to learn complex low-level skills. For example, LLMs can be used to design reward functions, the components that steer the robotics reinforcement learning (RL) system to find the correct sequences of actions for the desired task.

However, once a policy is learned in simulation, transferring it to the real world requires a lot of manual tweaking of the reward functions and simulation parameters.

The goal of DrEureka is to use LLMs to automate the intensive human efforts required in the sim-to-real transfer process.

DrEureka builds on Eureka, a technique that was introduced in October 2023. Eureka takes a robotic task description and uses an LLM to generate software implementations for a reward function that measures success in that task. These reward functions are then run in simulation and the results are returned to the LLM, which reflects on the outcome and modifies it to the reward function. The advantage of this technique is that it can be run in parallel with hundreds of reward functions, all generated by the LLM. It can then pick the best functions and continue to improve them.

While the reward functions of Eureka are great for training RL policies in simulation, it does not account for the messiness of the real world and therefore requires manual sim-to-real transfer. DrEureka addresses this shortcoming by automatically configuring domain randomization (DR) parameters.

DR techniques randomize the physical parameters of the simulation environment so that the RL policy can generalize to the unpredictable perturbances it meets in the real world. One of the important challenges of DR is choosing the right parameters and range of perturbations. Adjusting parameters requires commonsense physical reasoning and knowledge of the target robot.

These characteristics of designing DR parameters make it an ideal problem for LLMs to tackle because of their strong grasp of physical knowledge and effectiveness in generating hypotheses, providing good initializations to complex search and black-box optimization problems in a zero-shot manner, the researchers wrote.

DrEureka uses a multi-step process to break down the complexity of optimizing reward functions and domain randomization parameters at the same time. First, an LLM generates reward functions based on a task description and safety instructions about the robot and the environment. DrEureka uses these instructions to create an initial reward function and learn a policy as in the original Eureka. The model then runs tests with the policy and reward function to determine the suitable range of physics parameters, such as friction and gravity.

The LLM then uses this information to select the optimal domain randomization configurations. Finally, the policy is retrained with the DR configurations to become robust against the noisiness of the real world.

The researchers described DrEureka as a language-model driven pipeline for sim-to-real transfer with minimal human intervention.

The researchers evaluated DrEureka on quadruped and dexterous manipulator platforms, although the method is general and applicable to diverse robots and tasks. Their findings show that in quadruped locomotion, policies trained with DrEureka outperform the classic human-designed systems by 34% in forward velocity and 20% in distance traveled across various real-world evaluation terrains. They also tested DrEureka on dexterous manipulation with robotic hands. Given a fixed amount of time, the best policy trained by DrEureka performed 300% more cube rotations than human-developed policies.

But the most interesting finding was the application of DrEureka on the novel task of having a robo-dog balancing and walking on a yoga ball. The LLM was able to design a reward function and DR configurations that allowed the trained policy to be transferred to the real world with no extra configurations and perform well enough on diverse indoor and outdoor terrains with minimal safety support.

Interestingly the study found that the safety instruction included in the task description plays an important role in ensuring that the LLM generates logical instructions that transfer to the real world.

We believe that DrEureka demonstrates the potential of accelerating robot learning research by using foundation models to automate the difficult design aspects of low-level skill learning, the researchers wrote.

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The Howard D. McMillan Middle School robotics teams from Miami-Dade County Public Schools (M-DCPS) recently traveled to Dallas, TX, to compete in the VEX Robotics World Championship thanks to the generosity of the Kiwanis Club of Miami.

This is exactly the type of function we get involved in, said Bruce Ross, president of the 102-year Kiwanis Club of Miami, one of South Floridas most active community service organizations. Were so happy you qualified for this international competition in Dallas that we wanted to help you with this journey.

With that, Ross, Kiwanis treasurer Chuck Little and Kiwanis secretary Diana Morrell unwrapped a surprise oversized check for $6,000 that drew audible gasps from members of the robotics program.

The check topped off the year-around fundraising efforts of the West Kendall school and enabled it to send four four-person teams to compete, including the top team in Florida comprised of Ellis Lee, Rowan Liro, Andrew Rothen and Sirius Harmon. In addition, another four members of the program traveled with the competitors.

We were so excited to welcome the Kiwanis Club of Miami, said McMillan Middle School principal Dr. Justin Koren. This was an incredible opportunity for 20 of our students to travel to Dallas for the World Championship. Without a doubt, this is another reason Howard D. McMillan Middle School is the best choice for education.

Following the check presentation, members of the robotics teams demonstrated their robots to the Kiwanis officials and answered questions from the media.

As a way of saying thank you to the Kiwanis Club of Miami, the McMillan robotics teams placed Kiwanis International logos on their robots.

What the Kiwanis Club of Miami has done is simply fantastic, said robotics coach Dale Adamson. Not only did it allow us to send more teams that qualified but overall showed the students that people in the community care. That is an invaluable lesson that will stick with them forever.

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The growth opportunities for AI and robotics in manufacturing are vast and projected to reach $182 billion globally by 2027. Companies that fail to adopt these exponentially advancing technologies as part of their process risk falling behind, as smart factories powered by intelligent machines become the norm.

Artificial intelligence (AI) and robotics are revolutionizing manufacturing, providing scale, speed, and time-to-market advantages. According to the World Economic Forum, over 50% of manufacturers will adopt AI by 2025 as these technologies are proving effective in boosting shop floor efficiency, quality, and flexibility.

AI gives robotics programming and manufacturing platforms unprecedented adaptability, scale and speed. AI and robotics also are the catalysts that enable manufacturers to achieve more in less time and at a lower cost. Automating repetitive, dangerous, or ergonomically taxing manual tasks, including heavy lifting, materials handling, and assembly is just the start of these two technologies many contributions to manufacturing.

Combining artificial intelligence and robotics across shop floors also automates quality inspection, where AI helps reduce defects and scrap. The growth opportunities for AI and robotics in manufacturing are vast and projected to reach $182 billion globally by 2027. Companies that fail to adopt these exponentially advancing technologies as part of their process risk falling behind, as smart factories powered by intelligent machines become the norm.

Integrating Artificial Intelligence and robotics continues to revolutionize manufacturing by providing new automated methods to improve production efficiency and quality while reducing costs. Powered by the data AI and machine learning algorithms produce, robots can now perform complex manual tasks with unmatched precision and speed. This automation provides the accuracy, scale, and speed manufacturers need to stay competitive, meet customer delivery dates, and stay competitive during turbulent economic times. Key applications yielding major benefits include:

Robotic arms with sensors, computer vision, and artificial intelligence can take over heavy lifting, materials handling, assembly, and other repetitive manual work. DELMIA Robotics solutions provides the programming and simulation tools to enable this automation with material-handling robots. This frees the human workforce for higher-value functions and improves safety by reducing ergonomic strains. According to McKinsey, automating predictable physical work could boost global productivity by 0.8 to 1.4% annually.

Orchestrating AI and robotics in combination with manufacturing execution systems (MES) and enterprise resource planning (ERP) systems is defining the future of mass customization and design-for-manufacturing. These technologies enable manufacturers to produce customized products at scale profitably. AI automates repetitive design and production tasks, freeing engineers to focus on delivering faster innovation. DELMIA optimizes robotic routings, work instructions for each unique configuration, and is integral to ensuring mass customization and design-for-manufacturing strategies succeed.

Robotics manufacturing flexibly switches between real-time product variations while ensuring accuracy and precision. AI optimizes production plans and logistics in real time, tailoring them for mass customization. Intelligent design tools use AI to optimize manufacturability. Together, AI and robotics amplify the advantages of integrated design-to-manufacturing platforms, accelerating throughput, quality, and profitability. Manufacturers can now deliver large-scale, small-batch tailored products and capture more revenue opportunities.

DELMIA Robotics optimization leverages AI algorithms to analyze terabytes of manufacturing data, identifying patterns and anomalies for optimizing planning and scheduling in real time. AI-based DELMIA techniques effectively detect and predict potential production constraints and simulate scenarios to minimize downtime and bottlenecks.

Combining DELMIA Robotics AI-based manufacturing intelligence, robotics simulation, and integration with manufacturing execution systems (MES) and enterprise resource planning (ERP) further improves production yield rates and quality consistency across all production centers. When the insights from DELMIAs unified robotics, MES, and ERP data become ingrained, it leads to greater customer satisfaction, repeat sales, and profitability.

Combining IoT, IIoT, and Programmable Logic Controller (PLC) sensors and AI predictive analytics minimizes unplanned downtime of machinery, including robotics across shop floors. Algorithms detect early performance anomalies and predict maintenance needs before failures occur. Instead of rigid schedules, maintenance is performed only when required. According to PwC, AI-enabled predictive maintenance can reduce maintenance costs by 10-40% and decrease overall downtime by 20-50%. In one case, AI helped a rail company cut unplanned downtime by 35%, saving an estimated $50 million. As these examples highlight, predictive maintenance powered by AI delivers major efficiency and cost gains.

DELMIA robotics also helps validate production systems and robot programming within a 3D collaborative environment before building the production system using Virtual Twin techniques and technologies. It can also enable the virtual commissioning of robotics systems to confirm that they work as expected before the control program is downloaded to actual equipment. This can further reduce the risk of failures and optimize the performance of robotics systems. DELMIA robotics is a valuable tool for enhancing predictive maintenance with artificial intelligence in manufacturing.

Robots with computer vision and AI-based deep learning can inspect products for defects autonomously before they leave production centers, reducing the need for return material authorizations (RMAs) to customers. This lowers customer complaints and boosts customer loyalty and trust by catching defective products before shipment. AI-based algorithms learn from the data they capture, analyze, and act on, strengthening the quality muscle memory of production centers using these technologies. McKinsey found that advanced image recognition techniques for visual inspection and fault detection with AI can increase productivity by up to 50%. McKinsey also found that AI-based visual inspection with image recognition can improve defect detection rates by up to 90% compared to human inspection. Moreover, DELMIA Robotics software can help validate and optimize robot programming and simulation in a Virtual Twin 3D collaborative environment before building the production system. This can further enhance the performance and reliability of robots for quality inspection.

Manufacturing is transforming with AI and robotics, which offer scale, speed, and time-to-market advantages. The AI in manufacturing market size will grow from $2.3 billion in 2022 to $23.6 billion by 2030, with a 31.1% CAGR. The industrial robotics market will reach $86.6 billion by 2027, up from $50.3 billion in 2021. These numbers show how AI and robotics play a vital role in manufacturing.

AI and robotics power design-to-manufacturing platforms that optimize complex tasks, increase quality and productivity and streamline customized product development. Intelligent robots work with human workers, improving flexibility, precision, and safety. AI and robotics enable a company with agile manufacturing, which allows mass personalization and on-demand production, meeting customer demand and supply chain dynamics.

AI and robotics make the factories of the future smarter and more efficient. Manufacturers need to use these exponential technologies to gain a competitive advantage. DELMIA Robotics software helps manufacturers validate and optimize robot programming and simulation in a 3D collaborative environment before building the production system. This reduces the risk of failures, saves time and cost, and improves the performance and reliability of robots for various production processes.

Louis Columbus, is senior industry marketing manager at DELMIA. DELMIA empowers manufacturing, supply chain and service providers to efficiently plan, manage, optimize and execute their operations.

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The Interplay Between Robotics and Artificial Intelligence in Manufacturing | RoboticsTomorrow - Robotics Tomorrow

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Doosans PRIME-SERIES P3020 cobot has the longest reach of any of the companys cobots to date. | Source: Doosan Robotics

Doosan Robotics Inc. unveiled its latest and most powerful series of cobots yet: the PRIME-SERIES, or P-SERIES yesterday. Its new P3020 cobot, part of the line, is the companys longest-reaching cobot to date.

The P3020 collaborative robot (cobot) has a payload of up to 30 kg (60 lbs) and a reach of 2,030 mm (80 in). This means it can palletize from the floor to up to 2 m high, stacking up to 10 layers of boxes approximately 8 inches tall, using its simple fixed base without a lift.

As a motion platform company, Doosan Robotics is meeting the ever-growing need for cobots to mimic human motion, powerfully and safely, said William Ryu, CEO of Doosan Robotics. The robotics industry continues to grow at an exponential pace and our lineup of software, cobots and AI differentiates us in our mission to develop cobots with a max-powered, max-efficiency, max-safety mindset.

The South Korean-based cobot manufacturer said its robots can work in several industries, including manufacturing, logistics, food and beverage, architecture, filmmaking, service sectors, and medicine. Its H-SERIES cobot line, released in 2022, has attracted major global customers like Schiphol Airport and Wacker Korea. This has enhanced its global market share by 72%.

Doosans P-SERIES has low power consumption compared to similar payload cobots. It does this by applying its built-in gravity compensation mechanism, inherent wrist-singularity free, and a 5 degree-of-freedom movement with the 4th axis removed and 6th axis speed increased to 360 degrees/second.

The robot has also achieved the highest PL (e) and Cat 4 safety ratings to ensure both a max-powered and max-safety experience for users.

Doosan says its systems can adeptly maneuver through intricate scenarios while infusing tasks with enhanced efficiency, safety, and ingenuity. The AI systems behind the cobots have continuous learning capabilities and update its model by autonomously downloading required modules for smooth integration.

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Earlier this year, Doosan released two new products: its new Dart-Suite software and Otto Matic palletizing system. The company said the artificial intelligence powering its systems continuously learns and updates its model by downloading modules automatically.

Doosan described Dart-Suite as a robot ecosystem that redefines its customers experience with its robots and makes automation more accessible. The platform incorporates AI to enable users to develop, sell, download, and trigger actions through various interfaces. The suite uses an integrated development environment (IDE), which Doosan said allows customers to create modules tailored to their needs, similar to mobile device apps.

Otto Matic is a depalletizing and palletizing system that can handle unstructured and random-sized boxes. Doosan said it developed the system to bring additional deep learning and computer vision technology to its cobots.

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Doosan Robotics releases its longest reach cobots with PRIME SERIES - Robot Report

Summary

The company collaboration scales supply chain for FoxBot ATL andestablishes a nationwide service and support network for Autonomous Forklifts.

SUMMERVILLE, S.C. and AUSTIN, Texas - May 7, 2024 -KION North America(KION NA), manufacturer of Linde Material Handling equipment, andFox Roboticshave entered into a non-exclusive partnership wherein KION NA will manufacture and assemble FoxBot autonomous trailer loader/unloaders (ATLs) at its facilities in Summerville, South Carolina. We are at a pivotal moment in the logistics and transportation industry, where innovation is key to addressing some of the most pressing challenges we face. Specifically, in the realm of automated trailer loading and unloading, the last remaining piece of the end-to-end warehouse automation puzzle, Fox Robotics stands out by far as the dominant leader with the most robust AI/ML algorithms, tech stack, and deployed robot fleet weve ever seen, comments Jonathan Dawley, President and CEO at KION North America. We are delighted to announce our collaboration with Fox Robotics to launch this groundbreaking product in the U.S. market. This partnership signifies a major leap forward in our commitment to enhancing efficiency, safety, and reliability in our customers' operations. "Fox Robotics is scaling and expanding its supply chain capabilities significantly via the partnership with KION North America," said Marin Tchakarov, CEO and President at Fox Robotics. "Our traction in the marketplace is tremendous, and Fox Robotics is at an inflection point in its growth arc. We are experiencing unprecedented demand and have a massive list of bookings for Fox Robotics products and solutions. The partnership with KION NA will most certainly pave the way to capture all this commercial growth." TheFoxBot autonomous forkliftis the world's first Class 1 electric, stand-up autonomous forklift designed for load/unload operations on warehouse loading docks. Classified as an autonomous mobile robot (AMR), the FoxBot robotic forklift automates various operator tasks to improve workplace safety, increase productivity, and enhance employee satisfaction. The company started selling ATLs commercially in 2019 and is the first mover in the category. Recently, Fox Robotics announced that its installed base of FoxBot ATLs has processed nearly 3 million pallet pulls to date. "Thelogistics space is the single biggest market for automation for the next ten years. Converting the shipping and receiving dock, the gateway to the warehouse, from a manual operation to an automated one will drive the greatest growth and change,"said Till Reuter, board director for Fox Robotics and former CEO of Kuka Robotics. "Fox Robotics is poised to capture this growth in automation as the dominant leader in this space and the first one to deliver on the promise of true end-to-end automation of the warehouse of the future."

Headquartered in Summerville, S.C.,KION North America Corporationis a member of the KION Group, one of the world's leading manufacturers of industrial trucks. Their brand companies, Linde and Baoli, serve the specific industrial truck requirements of the US, Canadian, and Mexican markets with a broad and complementary product portfolio. KION North America produces material handling equipment known for its innovative technologies, reduced energy consumption, and low operating costs. KION North America also works closely with its sister company, Dematic, a global leader in automated material handling that provides a broad range of intelligent supply chain and automation solutions.

Founded in 2017, Fox Robotics is leading the industry in autonomous pallet workflows to improve workplace safety and productivity in warehouses across the U.S. and Canada. The company develops, manufactures, and sells the world's first autonomous trailer loader/unloader, the FoxBot. Fox Robotics is backed by investments from BMW i Ventures, Menlo Ventures, and Zebra Technologies Corp.

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KION North America and Fox Robotics Announce Non-Exclusive Strategic Partnership - Automation.com

A robot constructed by the Mars Robotics Association. Butler Eagle file photo

On Saturday, May 11, the public can visit Mars to experience the thrill of controlling a rover on the surface of Mars.

The Mars Robotics Association is hosting an event which will give the public a chance to control robots not too dissimilar to NASAs Perseverance rover, which currently is scanning the Red Planet for signs of life.

Were trying to simulate the same principles as the real rovers, said Jeff Beckstead, president of the Mars Robotics Association.

On Saturday, visitors can drive these robots across an 8-by-16-foot surface representing a portion of Mars, with an image taken from a real-life satellite capture of the planet. The robots were constructed of Lego bricks by members of the Mars Robotics Association.

These robot pilots are tasked with collecting rock samples and determining whether the samples are worthy of being investigated for signs of life.

Each one has a test tube thats inserted into it, and inside that test tube is a color that would represent that type of rock, Beckstead said. We go out, find rocks, then we bring them back to base. Then, we evaluate those to see if theyre valuable or not.

In so doing, they will simulate the Perseverance rovers mission to collect rocks to study for signs of life on Mars.

The Perseverance rover is collecting rock samples on Mars and putting them in sample tubes, Beckstead said. The next mission to Mars will bring those samples back to Earth for laboratory investigation.

Saturdays event is a collaboration between the Mars Robotics Association and NASAs Jet Propulsion Laboratory and also is part of Remake Learning Days, which is taking place across southwestern Pennsylvania through May 22. Remake Learning, a nonprofit educational network, provided the grant for this event.

Theyve been promoting a lot of different activities across the state that are focused on STEM education, Beckstead said. Mars Robotics was awarded a grant to help us support this activity.

The event will take place from 11 a.m. to 3 p.m. at the Mars municipal building, 301 Arch St. According to the events webpage, the event is targeted mainly at those ages 8 to 18.

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Mars Robotics offers chance to drive Mars rover on Saturday - Butler Eagle

image:

The group with the robot tutor made fewer errors and spoke more fluently than the group with human tutors, indicating the effectiveness of robot-assisted learning.

Credit: Takamasa Iio from Doshisha University Image Link: https://media.springernature.com/lw685/springer-static/image/art%3A10.1007%2Fs12369-024-01134-0/MediaObjects/12369_2024_1134_Fig1_HTML.jpg?as=webp

Advancements in large language models, robotics, and software such as text-to-speech, have made it possible to develop robots that can understand language, interact physically, and communicate verbally. These breakthroughs have opened up possibilities for robots to be used for educational purposes. However, this raises the question of whether robots are as good as human tutors. While robots offer certain benefits, they cannot replicate the nuanced interactions and personalized feedback human tutors provide.

To determine the suitability of using robots for education, Associate Professor Takamasa Iio from Doshisha University, along with Associate Professor Yuichiro Yoshikawa, Professor Hiroshi Ishiguro from Osaka University, and Associate Professor Kohei Ogawa from Nagoya University, Japan, compared the performance of current Robot-Assisted Language Learning (RALL) systems to human tutors. Their study published in the journal International Journal of Social Robotics on April 11, 2024, explored the effectiveness of each approach in improving students' English-speaking skills in second language learning. There has been an active movement to utilize robots in education, particularly in language education, which requires communication. However, it was not clear what benefits there would be in utilizing robots. We began this study with the belief that in order to expand the use of robots in the future, it would be necessary to compare the discussion with the baseline of human tutors, says Dr. Iio.

The researchers conducted an experiment involving 26 university students whose native language is Japanese. The students underwent a pre-test for an initial assessment of their English-speaking skills. Based on the average scores, the students were divided into two groups: 14 students received instruction from a robot, while the remaining 12 participants received online lessons from English language teachers. Over the course of seven days, both groups engaged in daily 30-minute sessions aimed at improving their English-speaking skills. The robot used was a table-top humanoid called CommU, which students could interact with using a tablet. This robot model could display human-like behaviors. It could nod to agree, slump to show thoughtfulness, and raise its hands to express joy or surprise.

In addition, there were three speaking exercises. The first involved roleplay, where the tutor (human or robot) and the student enacted a conversation in specific situations. The students listened to their scripts from the tutor and then repeated them. In the case of the robot, an audio file containing the speech of a native speaker was played instead of text-to-speech. The second exercise was flashcard practice, where students listened to their tutor deliver the script and repeated it to memorize it. In the final exercise, the students reenacted the conversation with their tutor using the memorized scripts. On the last day, participants underwent tests to assess their speaking errors, fluency, pronunciation, and speech complexity (number of words used in sentences).

All participants underwent a pre-test on the first day, followed by 30 minutes of daily study for 7 days, and three post-tests on the final day. The result indicated that the group taught by the robot made fewer errors and spoke more fluently than the group taught by human tutors. However, no significant difference was observed in outcomes between robots and human tutors for other aspects. The researchers believe that the improvement is because the students could practice the exercises with the robots much more than they could with the human tutor. This level of repetition improved their memory retention and speaking proficiency.Additionally, the expression of the robot may have reduced anxiety, allowing them to speak English without fear of judgment.

Social robots could be used more in second language learning. They are likely to play an active role in repetitive practice aimed at consolidating basic words, phrases, and grammatical structures in memory, highlights Dr. Iio.

While current RALL systems are sufficient to provide basic English training, the researchers predict that future systems will become more advanced.They may be able to recognize non-native speakers speech, offer corrections, and conduct interactive lessons and open-ended dialogues. However, since the fundamental purpose of language is communication with others, human tutors will remain essential for helping learners feel more comfortable and confident when using a new language in real-life situations.

About Associate Professor Takamasa Iio from Doshisha University, Japan Dr. Takamasa Iio received the Ph.D. degree from Doshisha University, Kyoto, Japan, in 2012. He has worked at ATR, Osaka University, and the University of Tsukuba. Currently, he is an associate professor at Doshisha University, Kyoto, Japan. His field of expertise is social robotics. He explores the impact of human-robot interaction on cognition and behavior, alongside societal shifts. His current research focuses on social robots that can enhance social capital in human society by mediating relationships between people.

Funding information This work was supported by JST Moonshot R&D Grant Number JPMJMS2011 (Analysis), JSPS KAKENHI Grant Number 19H05691 (Development of the robot system and the tablet interface), and the Benesse Corporation (Development of the learning materials and assessment of the tests).

Media contact: Organization for Research Initiatives & Development Doshisha University Kyotanabe, Kyoto 610-0394, JAPAN E-mailjt-ura@mail.doshisha.ac.jp

International Journal of Social Robotics

Observational study

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Comparison of Outcomes Between Robot-Assisted Language Learning System and Human Tutors: Focusing on Speaking Ability

11-Apr-2024

The authors declare that they have no conflict of interest.

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Comparative analysis of robot-assisted language learning systems and human tutors in English conversation lessons - EurekAlert