Robot Technologies: News and Product Announcements

Carbon Robotics raises $30 million in series C funding to scale LaserWeeder platform

Mike Oitzman — Wed, 12 Apr 2023 18:11:17 +0000

The Carbon Robotics LaserWeeder smart implement can autonomously remove unwanted plants from the crop field. | Credit: Carbon Robotics

Carbon Robotics closed $30 million in Series C financing from new lead investor Sozo Ventures along with existing investors Anthos Capital, Fuse Venture Capital, Ignition Partners, Liquid2 and Voyager Capital. The funding will be used to expand sales regions in North America, optimize and scale manufacturing, develop new software and hardware products, and launch into international markets. This latest round of financing brings Carbon Robotics’ overall funding to $67 million.

“Carbon Robotics’ elegant use of AI, computer vision, robotics and lasers is the only solution that enables farmers to reduce their most expensive line item – weed control – without damaging plants or the soil,” said Rob Freelen, managing director of Sozo Ventures. “I am particularly impressed with the team’s fast pace of innovation to bring breakthrough products to market, boosting farmers’ profitability across conventional, organic and no-till practices.”

The LaserWeeder identifies weeds and targets them for elimination. The implement’s 30 high-powered CO2 lasers use thermal energy to destroy the meristem of the weed with millimeter accuracy, without damaging nearby crops or disturbing the soil.

“This financing round further supports our mission to provide cost-effective and efficient precision ag-tech tools to growers,” said Paul Mikesell, CEO and founder of Carbon Robotics. “Traditional weeding methods, including hand weeding and herbicides, are expensive, unreliable and damage soil health. The LaserWeeder uniquely addresses all of these challenges.”

To date, Carbon Robotics’ LaserWeeders have successfully eliminated more than 500 million weeds across 40 different crops. This year, the LaserWeeder will be delivered to farms across 17 U.S. states and three provinces in Canada. Most recently, Carbon Robotics expanded the features of the LaserWeeder with the industry’s first LaserThinning capability, targeting areas where vegetable crops are purposefully overseeded and then thinned for optimal crop spacing, growth and yield.

As part of the Series C financing, Rob Freelen with Sozo Ventures and Erik Benson with Voyager Capital will join the Carbon Robotics board of directors.

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Agility Robotics’ Jonathan Hurst to keynote Robotics Summit

Brianna Wessling — Wed, 12 Apr 2023 18:07:02 +0000

Agility Robotics has been working since it spun out of Oregon State University in 2015 to bring humanoid robots out of the lab and into the real world. The company has made great strides in making its robots fit for bulk material handling tasks like tote movement.

Earlier this year, the company unveiled the latest generation of its Digit robot, built specifically to carry out tasks in warehouses and distribution centers. Updates in the next generation include newly designed end effectors optimized for reaching high/low, pulling, picking up, and placing objects commonly found in e-commerce and shipping warehouses, like plastic totes. Digit also has a new head with LED animated eyes, which allow for improved human-robot interaction such as using simple expressions to convey information and intent.

In his keynote talk at the Robotics Summit & Expo titled “Developing Human-Centric Bipedal Robots,” Agility co-founder and Chief Technology Officer Jonathan W. Hurst will give an inside look at the process of developing bipedal robots that are useful in the workplace. The Robotics Summit will take place at the Boston Convention Center on May 10-11, 2023, and Hurst will give his talk at 10:45 AM on the show’s second day.

Hurst is also a Professor and co-founder of the Oregon State University Robotics Institute. He holds a B.S. in mechanical engineering and an M.S. and Ph.D. in robotics, all from Carnegie Mellon University. His university research focuses on understanding the fundamental science and engineering best practices for robotic-legged locomotion and physical interaction. Agility Robotics is bringing this new robotic mobility to market, solving customer problems, working towards a day when robots can go where people go, generate greater productivity across the economy, and improve the quality of life for all.

You can find the full agenda for the Robotics Summit here. The Robotics Summit & Expo is the premier event for commercial robotics developers. There will be nearly 70 industry-leading speakers sharing their development expertise on stage during the conference, with 150-plus exhibitors on the show floor showcasing their latest enabling technologies, products and services that help develop commercial robots. There also will be a career fair, networking opportunities and more.

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Comau develops mobile manipulator

Brianna Wessling — Tue, 11 Apr 2023 20:55:37 +0000

Comau’s mobile manipulator uses a Racer-5 cobot and a 1500 autonomous guided vehicle (AGV). | Source: Comau

Comau has been developing a mobile manipulator that is being applied within three different EU projects.

The company’s Mobile Robotic Arm involves a robotic arm mounted on an autonomous mobile platform. The system is integrated with Comau’s Racer-5 collaborative robot (cobot), a six-axis articulated robotic arm that can work at speeds of up to 6 m/s when human operators aren’t present. The Racer-5 is mounted onto a 1500 autonomous guided vehicle (AGV).

The system can be integrated within Comau’s digital infrastructure and can provide visual feedback for pick and place operations, and more, with its integrated vision system. This includes a Comau MI.RA that is installed directly into the robotic arm.

The system’s AGV is equipped with two independent batteries that power the AGV and the robotic arm separately. The AGV can also be managed using different types of navigation modes and a standard Comau controller.

Comau’s first application for the Mobile Robotic Arm is with DIMOFAC, an EU initiative that aims to help companies implement a smart factory architecture. There, the platform is used for pick and place and warehouse automation tasks within a machining scenario.

The PeneloPe Project, another EU project, uses the platform for glue dispensing and non-destructive quality inspection in the public transport domain. The goal of the program is to develop a closed-loop, end-to-end digital manufacturing solution that facilitates bidirectional data flows across the manufacturing value chain.

Finally, Comau’s platform is being used as part of the ODIN project to support the manipulation of mechanical parts for automotive applications with the aim of demonstrating the technical and performance feasibility of collaborative robotics on the factory floor.

Earlier this year, Comau entered into a cooperative agreement with Siemens to offer their jointly engineered product the Sinumerik Run MyRobot / DirectControl. With this product, robot kinematics can be fully integrated into a CNC system, optimizing control of all robotic machining and handling tasks.

Comau is based in Turin, Italy and was founded in 1973. It recently launched a new robotics learning center with Ferrari. The e.DO Learning Center will use Comau’s robots to help students explore STEM subjects, coding and robotics. The facility is equipped with five of Comau’s e.DO 6-axis robots, complete with all necessary materials and accessories.

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Amazon’s ARMBench dataset helps train pick-and-place robots

Brianna Wessling — Tue, 11 Apr 2023 20:02:54 +0000

Amazon has released a dataset that contains images of more than 190,000 objects that it said can be used to train robots for pick-and-place tasks. Amazon claims this is the largest dataset of images captured in an industrial product-sorting setting.

The dataset, called ARMBench, can be used to train pick-and-place robots to better generalize new objects and contexts. The images were collected in an Amazon warehouse where a robotic arm retrieves a single item from a bin full of items and then transfers it to a tray on a conveyor belt. This task can be difficult because of the variety of objects in the bin and their various configurations and interactions.

Images in the dataset fall into three categories:

Pick images: top-down images of a bin filled with items before a robot starts picking
Transfer images: images captured from multiple viewpoints as the robot transfers an item to the tray
Place images: top-down images of the tray in which the selected item is placed

ARMBench contains images from three separate tasks, object segmentation, object identification and defect detection.

The object detection dataset, which helps robots identify the boundaries of different products in the same bin, contains more than 50,000 images. The images show anywhere from one to 50 manual object segmentations per image, with an average of about 10.5.

The object segmentation dataset helps robots determine which product image in a reference database corresponds to the highlighted product in an image. This dataset includes more than 235,000 labeled pick activities, with each pick activity including a pick image and three transfer images. This dataset also includes reference images and text descriptions of more than 190,000 products. Models can learn to match one of these reference products to an object highlighted in pick and transfer images.

From left to right: a pick image, a transfer image and place image from Amazon’s ARMBench dataset. | Source: Amazon

The defect detection dataset, which includes both images and videos, helps systems know when a robot has committed an error, like picking up multiple items rather than one or damaging an item during transfer. The dataset has more than 19,000 images captured during the transfer phase. It also includes more than 4,000 videos that document pick-and-place activities that resulted in damage to a product.

Videos are a key aspect of this dataset, as certain types of product damage are best diagnosed through video, as they can occur at any point in the transfer process. The defect detection dataset also contains images and videos for over 100,000 pick-and-place activities without defects.

Amazon plans to continue to expand the number of images and videos, and the range of products they depict, in ARMBench.

In November 2022, Amazon unveiled Sparrow, a robotic arm capable of picking individual products before they get packaged. Sparrow can pick 65% of the over 100 million different items that could be processed at an Amazon warehouse, according to the company.

Sparrow can pick a variety of items, like DVDs, socks and stuffed animals, but struggled with items that have loose or complex packaging. It seems likely the company drew on the research it did while developing Sparrow to build this dataset.

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Covariant raises $75M for robotic picking technology

Brianna Wessling — Tue, 11 Apr 2023 00:21:22 +0000

Covariant raised another $75 million in its Series C funding round. The company announced the first part of its series C round, totaling $80 million, in 2021. It has now raised $222 million since its founding.

Radical Ventures and Index Ventures, both returning investors in the company, co-led the round. The most recent round also included participation from other returning investors Canada Pension Plan Investment Board and Amplify partners, and new investors Gates Frontier Holdings, AIX Ventures and Northgate Capital.

Since its founding in 2017, Covariant has focused on developing the Covariant Brain, which the company called a Universal AI platform. Pre-trained on millions of picks from Covariant robots in warehouses around the world, the Covariant Brain enables robots to autonomously pick many SKUs. Covariant plans to use the funding to ensure its platform will allow retail and logistics providers to deploy robotic picking quickly.

“The leading companies have turned to AI Robotics to automate their most manual operations in order to decrease labor costs, increase throughput, and control profitability,” said Covariant CEO Peter Chen. “The past year for Covariant has been incredible with 6x growth in 2022 – and we are just getting started. This infusion of new capital allows us to scale even faster, ensuring more retailers can automate more parts of their fulfillment networks to remove manual bottlenecks, handle fluctuating demand, and better prepare for ever-changing business needs.”

Since its last funding announcement in 2021, the company has applied Covaraint Brian to a broad set of piece-picking and case-picking applications, including order sortation, item induction, good-to-person order picking, knitting and depalletization. With the platform, connected robots learn as a fleet, enabling operational improvements to automatically propagate across customers’ networks.

“Many companies are trying to break into the AI robotics space, but Covariant has been making significant progress for years now,” said Mike Volpi, partner at Index Ventures. “I’m confident that their team, which represents the best minds in AI, and their approach of deploying a unified AI platform are shaping the future of automation, and look forward to the additional progress they’ll make in the years ahead.”

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Cruise recalls 300 robotaxis in response to crash with bus

Brianna Wessling — Mon, 10 Apr 2023 13:21:24 +0000

A Cruise robotaxi failed to brake quickly enough behind a city bus in San Francisco. | Source: Cruise

Cruise issued a voluntary recall with the National Highway Transporation Safety Administration (NHTSA) in response to a minor collision where a Cruise autonomous vehicle (AV) hit the back of a San Francisco bus. The recall affects 300 AVs.

The Cruise AV involved in the crash failed to brake quickly enough after the city bus in front of it slowed, according to the company. While the vehicle did brake, it applied the brakes too late and rear-ended the bus at about 10 MPH, Cruise said.

After the collision, Cruise began an investigation and found the cause of the crash was an error related to predicting the movement of articulated vehicles, which are vehicles with two sections connected by a flexible joint allowing them to bend in the middle, like the bus in the accident.

According to Cruise, the robotaxi saw the front section of the bus and recognized that it was an articulated vehicle that could bend, so it predicted the bus would move as connected sections with the rear section following the predicted path of the front section. As the bus pulled out, Cruise said the AV reacted based on the predicted actions of the front end of the bus, which it could no longer see, rather than the actual actions of the rear section of the bus, making it slow to brake.

Once the company found the root cause of the accident, it started working on a software update that it said would improve performance near articulated vehicles. When the update was completed, tested and validated, Cruise’s operations team rolled the change out to the fleet, just two days after the incident occurred. Results from testing indicate the specific issue that caused the accident won’t recur after the update.

“Our data and simulations showed that it was exceptionally rare. At the time of the incident, our AVs had driven over 1 million miles in fully driverless mode. We had no other collisions related to this issue, and extensive simulation showed that similar incidents were extremely unlikely to occur at all, even under very similar conditions,” Cruise founder and CEO Kyle Vogt wrote in a blog. “The collision occurred due to a unique combination of specific parameters such as the specific position of the vehicles when the AV approached the bus (with both sections of the bus visible initially, and then only one section), the AV’s speed, and the timing of the bus’s deceleration (within only a few seconds of the front section becoming occluded).

“We will undoubtedly continue to discover ways in which we can improve, even if that involves changing software that is currently deployed in the field,” he continued. “We think any potential improvement to roadway safety is worthwhile, and we will approach it with the same level of rigor as we’ve demonstrated here. These continuous improvements are likely to make voluntary recalls commonplace. We believe this is one of the great benefits of autonomous vehicles compared to human drivers; our entire fleet of AVs is able to rapidly improve, and we are able to carefully monitor that progress over time.”

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A roadmap to successfully commercializing robots

Steve Crowe — Mon, 10 Apr 2023 13:08:15 +0000

From left to right: Jennifer Apicella, Mike Dooley, Reese Mozer and Tom Ryden.

Commercialization is the impact point where all expectations for your product are tested – right when it hits the market. Successful commercialization is good for the individual company and the robotics industry as a whole. Success leads to more products – whether they are follow-on, derivative, or brand-new new ideas.

In the panel session “Robotics Roadmap to Commercialization Success” during the Robotics Summit & Expo on May 10 at 11:45 AM, learn from four robotics industry experts about developing strategic partnerships to expedite commercial expansion and the steps required to succeed in this process. Gain insights on navigating the non-linear road to commercialization as these executives share their experience and thoughts on the need for precision and detail-oriented partners.

The session will include Jennifer Apicella, Vice President of the Pittsburgh Robotics Network, where she oversees the growth and development of strategic partnerships and major programs, for one of the top robotics ecosystems in the world. With 15-plus years of experience working in technology, she has spent the majority of her career helping clients succeed with enterprise technology solution adoption.

It will also include Mike Dooley, a leading pioneer in consumer robotics, successfully shipping millions of products into homes for over the past 20 years. He launched the original LEGO MindStorms product line, introducing millions of kids to robotics. Dooley later co-invented and launched what is now the Braava series of smart robotic mops (acquired by iRobot) and helped develop the systematic mapping behaviors used in Roomba today. In 2017, he co-founded Labrador Systems to establish a new category of assistive robots.

Reese Mozer, CEO and Co-Founder of American Robotics and President of Ondas Holdings will also be a panelist. He is an accomplished entrepreneur, executive, and technologist, with over 12 years of experience in the drone, robotics, and private equity sectors. He also serves as a Board member of the Commercial Drone Alliance (CDA). Mozer holds a Master’s in Robotic Systems Development from Carnegie Mellon University and a B.S. in Mechanical Engineering and Entrepreneurship from the University of Southern California.

Tom Ryden, Executive Director of MassRobotics, will moderate the session. Ryden is an experienced senior executive who has devoted his career to creating viable businesses based on technology, covering domains ranging from the factory to battlefield to healthcare. He has overseen the development and introduction of leading mobile robotics products.

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Gatik to start deliveries with Kroger in Texas

Brianna Wessling — Sun, 09 Apr 2023 10:06:38 +0000

Gatik recently announced a multi-year commercial collaboration with Kroger to transport customer orders within Kroger’s Dallas distribution network.

Starting sometime in Q2 of 2023, Gatik’s medium-duty autonomous box trucks will be transporting products from a Kroger Customer Fulfillment Center (CFC) in Dallas, Texas to multiple retail locations. Gatik’s autonomous trucks will operate with safety drivers in the vehicles for now, but the company plans to eventually take them out.

Gatik’s trucks feature a cold chain-capable 20′ foot box to transport ambient, refrigerated, frozen goods. The collaboration involves consistent, repeated delivery runs multiple times per day, seven days per week, across Kroger’s Dallas distribution network. Each trip, from a CFC to a retail location, typically involves around 60 miles of driving round trip, according to Gatik’s Head of Policy and Communications Richard Steiner.

The company has always been interested in automating the middle mile. Steiner joined the company in 2019, just two years after it was founded, and has helped it grow into a now 150-person team.

“What we’ve seen over the last few years, even prior to the pandemic, is e-commerce going through the roof,” Steiner said. “Consumers like you and me no longer want to wait three, four, or five days for goods, we want them within a one to two-hour pickup window.”

According to Steiner, this change in the way people shop has led to smaller distribution centers, more micro fulfillment centers and more customer fulfillment centers closer to where those customers live. This is where automating the middle mile, between these fulfillment centers and retail locations, can greatly increase the speed and number of orders fulfilled.

“A really, really critical point to note about what we’re doing there is we’re increasing the delivery frequency,” Steiner said. “So that means that Kroger’s customers have a greater range of same-day pickup times as well as greater flexibility with cut-off times to place their orders.”

Automating the middle mile also means getting autonomy into the hands of Gatik’s customers quickly, because the team only needs to operate its AVs on repeatable routes instead of anywhere in a city a person might want to go.

“Compared to the other applications of technology, either long haul or passenger transportation, [middle-mile transportation] is simpler,” Steiner said. “We are constraining the challenge of autonomy by focusing on fixed known repeatable point-to-point routes. So whereas the passenger transportation models require mapping out an exponentially larger and larger geo-fenced area to serve an increasing number of consumers . . . We focus on a limited number of pickup locations and drop off locations, which means that we know our routes more intimately than anyone else on the planet.”

While Gatik doesn’t have a specific timeline for when it will be able to pull out its safety drivers from its trucks in Texas, with each deployment, including its deployment with Walmart in Arkansas and with Loblaw in Ontario, the process keeps moving faster.

“With Walmart in Arkansas, we began commercial operations in June of 2019. We pulled the driver out in August of 2021, so about two years right?” Steiner said. “With Loblaw, we began commercial operations in January 2020 and pulled the safety driver out 19 months after that. So we went from 24, 25 months to 19.”

Gatik has three phases that it works through for deployments. The first involves millions of miles of simulated training, which is followed by the second phase, which involves private closed course track testing, first with safety drivers and then without. Finally, the Gatik team moves into its public testing phase.

With or without a safety driver, Gatik’s trucks begin generating revenue for its customers on day one.

“We’ve now done over half a million customers orders across our customer base in North America,” Steiner said. “Every single one of those deliveries has been revenue generating. So we’re not testing for testing’s sake. This is a real business. This is a revenue-generating business.”

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MIT researchers create algorithm to stop drones from colliding midair

Brianna Wessling — Sat, 08 Apr 2023 14:00:32 +0000

The MIT team tested its collision avoidance system in a flight environment with six drones and in simulation. | Source: MIT

A research team from MIT created a trajectory-planning system called Robust MADER that can allow drones working together in the same airspace to pick safe paths forward without crashing into each other. The algorithm is an updated version of MADER, a 2020 project that worked well in simulation but didn’t hold up in real-world testing.

The original MADER system involved each agent broadcasting its trajectory so fellow drones know where it’s planning to go. In simulation, this worked without problems, with all drones considering each other’s trajectories when planning their own. When put to the test, the team found that it didn’t take into account delays in communication between drones, resulting in unexpected collisions.

“MADER worked great in simulations, but it hadn’t been tested in hardware. So, we built a bunch of drones and started flying them. The drones need to talk to each other to share trajectories, but once you start flying, you realize pretty quickly that there are always communication delays that introduce some failures,” Kota Kondo, an aeronautics and astronautics graduate student, said.

Robust MADER is able to generate collision-free trajectories for drones even when there is a delay in communications between agents. The system is an asynchronous, decentralized, multiagent trajectory planner, meaning each drone formulates its own trajectory and then checks with drones nearby to ensure it won’t run into any of them.

The drones optimize their new trajectories using an algorithm that incorporates the trajectories they received from nearby drones, and agents constantly optimize and broadcast new trajectories to avoid collisions.

To get around any delays in sharing trajectories, every drone has a delay-check period, where it spends a fixed amount of time repeatedly checking for communications from other agents to see if its new trajectory is safe. If it finds a possible collision, it abandons the new trajectory and keeps going on its current one. The length of this delay-check period depends on the distance between agents and other environmental factors that could hamper communications.

While the system does require all drones to agree on each new trajectory, they don’t all have to agree at the same time, making it a scalable system. It could be used in any situation where multiple drones are working together in the same airspace like spraying pesticides over crops.

The MIT team ran hundreds of simulations in which they artificially introduced communication delays, and found that MADER was 100% successful at avoiding collisions. When tested with six drones and two aerial obstacles in a flight environment, Robust MADER was able to avoid all collisions, while the old algorithm would have caused seven collisions.

Moving forward, the research team hopes to put Robust MADER to the test outdoors, where obstacles can affect communications. They also hope to outfit drones with visual sensors so they can detect other agents or obstacles, predict their movements and include that information in trajectory optimizations.

Kota Konda wrote the paper with Jesus Tordesillas, a postdoc; Parker C. Lusk, a graduate student; Reinaldo Figueroa, Juan Rached, and Joseph Merkel, MIT undergraduates; and senior author Jonathan P. How, the Richard C. Maclaurin Professor of Aeronautics and Astronautics, a principal investigator in the Laboratory for Information and Decision Systems (LIDS), and a member of the MIT-IBM Watson AI Lab. This work was supported by Boeing Research and Technology.

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Roboto AI raises $4.8M to build data tools for robotics developers

Brianna Wessling — Thu, 06 Apr 2023 19:28:32 +0000

A screenshot of the Roboto platform.

Roboto AI, a company working to create tools to help robotics developers go from a prototype to a reliable production system, emerged from stealth with $4.8 million in Seed funding. Unusual Ventures led the round, which also included the Allen Institute for Artificial Intelligence (AI2) and FUSE Ventures. Roboto AI is also partnering with researchers and professors at the ETH Zürich AI Center.

Robotics companies oftentimes have to develop all of their infrastructure, anything from custom hardware to data infrastructure in-house. This could cost startups crucial time and money that could be spent getting their systems into the world. Roboto AI hopes to make development easier by providing a platform that supports robotics data.

The company’s founders, Benji Barash (CEO) and Yves Albers-Schoenberg (CTO) have experience building this kind of infrastructure from their time at Amazon Robotics. The two used to spend entire days writing scripts to filter and transform sensor data so they could debug system failures, evaluate performance and create new algorithms.

Roboto AI is working to create a data platform that allows users to easily search across modalities using natural language. The company currently has a free demo available that uses data from nuScenes, a large-scale, autonomous driving dataset used by engineers and researchers, for those interested in testing an early version of Roboto. With the sandbox, users can not only use natural language to search for the data they need, but they can also perform searches on graphical time-series signals.

Roboto AI was founded in 2022 when it spun out of the Allen Institute for Artificial Intelligence. It is based in Seattle, Washington.

“Robotics is hard, but it shouldn’t be this hard. We want to catalyze progress in the robotics industry and see robots get to production faster by getting AI to do the dirty work for us,” the founders wrote in a blog. “We’re building Roboto to be an out-of-the-box solution to the data challenges engineers face daily and we’re excited to have the support of an incredible group of advisors and investors to make it happen. We’re building new AI-powered tools for anyone working with sensor or log data; think copilot for robotics.”

Roboto AI CTO Yves Albers-Schoenberg (left) and CEO Benji Barash. | Source: Roboto AI

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How MIT taught a quadruped to play soccer

Brianna Wessling — Thu, 06 Apr 2023 01:14:38 +0000

A research team at MIT’s Improbable Artificial Intelligence Lab, part of the Computer Science and Artificial Intelligence Laboratory (CSAIL), taught a Unitree Go1 quadruped to dribble a soccer ball on various terrains. DribbleBot can maneuver soccer balls on landscapes like sand, gravel, mud and snow, adapt its varied impact on the ball’s motion and get up and recover the ball after falling.

The team used simulation to teach the robot how to actuate its legs during dribbling. This allowed the robot to achieve hard-to-script skills for responding to diverse terrains much quicker than training in the real world. Because the team had to load its robot and other assets into the simulation and set physical parameters, they could simulate 4,000 versions of the quadruped in parallel in real-time, collecting data 4,000 times faster than using just one robot. You can read the team’s technical paper called “DribbleBot: Dynamic Legged Manipulation in the Wild” here (PDF).

DribbleBot started out not knowing how to dribble a ball at all. The team trained it by giving it a reward when it dribbles well, or negative reinforcement when it messes up. Using this method, the robot was able to figure out what sequence of forces it should apply with its legs.

“One aspect of this reinforcement learning approach is that we must design a good reward to facilitate the robot learning a successful dribbling behavior,” MIT Ph.D. student Gabe Margolis, who co-led the work along with Yandong Ji, research assistant in the Improbable AI Lab, said. “Once we’ve designed that reward, then it’s practice time for the robot. In real time, it’s a couple of days, and in the simulator, hundreds of days. Over time it learns to get better and better at manipulating the soccer ball to match the desired velocity.”

The team did teach the quadruped how to handle unfamiliar terrains and recover from falls using a recovery controller build into its system. However, dribbling on different terrains still presents many more complications than just walking.

The robot has to adapt its locomotion to apply forces to the ball to dribble, and the robot has to adjust to the way the ball interacts with the landscape. For example, soccer balls act differently on thick grass as opposed to pavement or snow. To combat this, the MIT team leveraged cameras on the robot’s head and body to give it vision.

While the robot can dribble on many terrains, its controller currently isn’t trained in simulated environments that include slopes or stairs. The quadruped can’t perceive the geometry of terrain, it just estimates its material contact properties, like friction, so slopes and stairs will be the next challenge for the team to tackle.

The MIT team is also interested in applying the lessons they learned while developing DribbleBot to other tasks that involve combined locomotion and object manipulation, like transporting objects from place to place using legs or arms. A team from Carnegie Mellon University (CMU) and UC Berkeley recently published their research about how to give quadrupeds the ability to use their legs to manipulate things, like opening doors and pressing buttons.

The team’s research is supported by the DARPA Machine Common Sense Program, the MIT-IBM Watson AI Lab, the National Science Foundation Institute of Artificial Intelligence and Fundamental Interactions, the U.S. Air Force Research Laboratory, and the U.S. Air Force Artificial Intelligence Accelerator.

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Capra Robotics’ AMRs to use RGo Perception Engine

The Robot Report Staff — Wed, 05 Apr 2023 21:19:21 +0000

RGo Robotics, a company developing artificial perception technology that enables mobile robots to understand complex surroundings and operate autonomously, announced significant strategic updates. The announcements include leadership appointments, new customers and an upcoming product release.

RGo develops AI-powered technology for autonomous mobile robots, allowing them to achieve 3D, human-level perception. Its Perception Engine gives mobile robots the ability to understand complex surroundings and operate autonomously. It integrates with mobile robots to deliver centimeter-scale position accuracy in any environment. In Q2 2023, RGo said it will release the next iteration of its software that will include:

An indoor-outdoor mode: a breakthrough capability for mobile robot navigation allows them to operate in all environments – both indoors and outdoors.
A high-precision mode that enables millimeter-scale precision for docking and similar use cases.
Control Center 2.0: a redesigned configuration and admin interface. This new version supports global map alignment, advanced exploration capabilities and new map-sharing utilities.

RGo separately announced support for NVIDIA Jetson Orin System-on-Modules that enables visual perception for a variety of mobile robot applications.

RGo will exhibit its technology at LogiMAT 2023, Europe’s biggest annual intralogistics tradeshow, from April 25-27, in Stuttgart, Germany at Booth 6F59. The company will also sponsor and host a panel session “Unlocking New Applications for Mobile Robots” at the Robotics Summit and Expo in Boston from May 10-11.

Leadership announcements

RGO also announced four leadership appointments. This includes Yael Fainaro being named chief business officer and president; Mathieu Goy being named head of European sales; Yasuaki Mori being named executive consultant, APAC market development; and Amy Villeneuve as a member of the board of directors.

“It is exciting to have reached this important milestone. The new additions to our leadership team underpin our evolution from a technology innovator to a scaling commercial business model including new geographies,” said Amir Bousani, CEO and co-founder, RGo Robotics.

Goy, based in Paris, and Mori, based in Tokyo, join with extensive sales experience in the European and APAC markets. RGo is establishing an initial presence in Japan this year with growth in South Korea planned for late 2023.

“RGo has achieved impressive product maturity and growth since exiting stealth mode last year,” said Fainaro. “The company’s vision-based localization capabilities are industrial-grade, extremely precise and ready today for even the most challenging environments. This, together with higher levels of 3D perception, brings tremendous value to the rapidly growing mobile robotics market. I’m looking forward to working with Amir and the team to continue growing RGo in the year ahead.”

Villeneuve joins RGo’s board of directors with leadership experience in the robotics industry, including her time as the former COO and president of Amazon Robotics. “I am very excited to join the team,” said Villeneuve. “RGo’s technology creates disruptive change in the industry. It reduces cost and adds capabilities to mobile robots in logistics, and enables completely new applications in emerging markets including last-mile delivery and service robotics.”

Customer traction

After comprehensive field trials in challenging indoor and outdoor environments, RGo continued its commercial momentum with new customers. The design wins are with market-leading robot OEMs across multiple vertical markets ranging from logistics and industrial autonomous mobile robots, forklifts, outdoor machinery and service robots.

Capra Robotics, an award-winning mobile robot manufacturer based in Denmark, selected RGo’s Perception Engine for its new Hircus mobile robot platform.

“RGo continues to develop game-changing navigation technology,” said Niels Juls Jacobsen, CEO of Capra and founder of Mobile Industrial Robots. “Traditional localization sensors either work indoors or outdoors – but not both. Combining both capabilities into a low-cost, compact and robust system is a key aspect of our strategy to deliver mobile robotics solutions to the untapped ‘interlogistics’ market.”

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Robotics Summit: How to use simulation for developing robots

Brianna Wessling — Wed, 05 Apr 2023 17:34:06 +0000

Warehouse logistics and advanced manufacturing are increasingly using robotics as a critical part of their automation strategies. Robots can improve operational efficiency, improve safety, and help companies address the persistent labor shortages that are being observed across the globe. Developing these intelligent robotic systems, however, is a complex, challenging, and costly undertaking. Thankfully, advanced simulation tools are available to engineers that can speed the design, development, and testing processes.

Gerard Andrews, Senior Product Marketing Manager of Robotics at NVIDIA, will discuss how to use NVIDIA’s Isaac Sim for robotics development. His talk at the Robotics Summit & Expo called “Using Simulation to Design and Develop Autonomous Robots” will take place at the Boston Convention Center on May 11 at 11:30 AM.

This talk will describe the many ways NVIDIA Isaac Sim can be used to accelerate the development and deployment of robots, including advanced AI and computer vision. Specifically, attendees will learn how simulation can test robot applications in photo-realistic, physically accurate digital twin environments. In addition, the robots can be placed in increasingly complex simulations involving digital humans and fleets of robots to optimize operational KPIs. This session is designed as an introduction to photo-realistic 3D simulation for robots and is appropriate for all levels.

Andrews is focused on revolutionizing the way intelligent robots are developed, trained, tested and deployed by driving the adoption of the NVIDIA Isaac Robotics platform. Prior to joining NVIDIA, he was at Cadence where he was Product Marketing Director, responsible for product planning, marketing, and business development for licensable processor IP. He holds an MS in electrical engineering from the Georgia Institute of Technology and a BS in electrical engineering from Southern Methodist University.

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Yaskawa releases MotoPick 4 software & robotic pallet builder

The Robot Report Staff — Tue, 04 Apr 2023 22:20:01 +0000

Yaskawa’s Pallet Builder extension (left) and a Yaskawa robot using the company’s new MotoPick 4 software (right). | Source: Yaskawa

Yaskawa released its MotoPick 4 software system that adds no-code capabilities for high-speed pick and place. The company is also expanding its Smart Pendant’s capability with its new Pallet Builder extension.

With the building blocks to create systems for up to 16 robots and up to 6 conveyors or 16 tables per robot, the Yaskawa MotoPick 4 software can synchronize multiple robots equipped with vision to pick fast-moving products off a conveyor and place them on an outfeed device, tray or box in an organized arrangement.

MotoPick 4 provides precise control and coordination for conveyor speed, accommodating tracking speeds up to 1 M/sec. If the advancement of either the infeed or outfeed conveyor falls behind, the software can be configured with an area that slows or stops conveyors until the operation is equalized. Additionally, the software features a pause function that allows for the system to be paused and later resumed without losing track of item positions.

Dynamic load balancing for single or multiple robot systems is accommodated. The ability to asynchronously pick up to four parts with multiple placements per cycle is also offered, with up to 32 different product types to be easily picked and sorted. The gripper is allowed up to eight zones.

Supporting multiple encoders and cameras, a robot interface board reduces the integration wiring and panel real estate required for multiple robot cells. Cameras are supported based on conveyor width, workpiece size, target accuracy, production volume and conveyor speed, and they do not need to be aligned in a pre-defined way.

Multi-layer tray support allows multiple-layer pattern picking or placing with trays. MotoPick 4 is compatible with the FS100 and YRC1000micro controllers, as well as the MLX300 controller software solution.

Pallet Builder

Expanding the Smart Pendant capability, Pallet Builder enables the development and deployment of palletizing cells. Designed for single pick-and-place tasks, this user interface uses a robust database and guided prompts to facilitate no-code programming for work cell configuration and pallet pattern setup.

Designed for small- to medium-sized enterprises with high-mix, low-to-medium-volume production that may have insufficient access to experienced robot programmers, Pallet Builder provides general functions for cell definition, gripper definition, and single pick and place jobs. Multiple infeeds, build stations and dispenser stations (for slip sheet separators) support up to eight combined stations.

Job editing is available for specific system requirements. Pallet Builder can accommodate a variety of grippers and end-of-arm tooling and adjusts for product variation. Capable software includes multiple automatic pattern styles as well as full custom pattern support. Single drop positions with selectable label orientation are also available.

Compatible with the YRC1000 and YRC1000 micro controllers, Pattern Builder is available as a complimentary download for use with GP-series and HC-series robots. Smart Pendant V3.0 or newer is required.

Yaskawa Motoman launched its newest robot in the PL-series line, the PL800, in October 2022. This industrial robot features an 800 kg (2,140 lb) payload capacity and is designed for a variety of palletizing applications, layer picking, and other logistical tasks for end-of-line or distribution automation.

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Robot Technologies: News and Product Announcements

Carbon Robotics raises $30 million in series C funding to scale LaserWeeder platform

Agility Robotics’ Jonathan Hurst to keynote Robotics Summit

Comau develops mobile manipulator

Amazon’s ARMBench dataset helps train pick-and-place robots

Covariant raises $75M for robotic picking technology

Cruise recalls 300 robotaxis in response to crash with bus

A roadmap to successfully commercializing robots

Gatik to start deliveries with Kroger in Texas

MIT researchers create algorithm to stop drones from colliding midair

Roboto AI raises $4.8M to build data tools for robotics developers

How MIT taught a quadruped to play soccer

Capra Robotics’ AMRs to use RGo Perception Engine

Leadership announcements

Customer traction

Robotics Summit: How to use simulation for developing robots

Yaskawa releases MotoPick 4 software & robotic pallet builder

Pallet Builder

Top 10 robotics stories of March 2023

10. Advanced Construction Robotics launches rebar installing robot

9. Built Robotics develops autonomous solar piling robot

8. Plus One Robotics raises $50M Series C funding round

7. Robotics investments reach $521M in January

6. ABB spending $20M to expand U.S. robotics factory

5. Next-gen Digit humanoid wants to automate logistics tasks

4. Slip Robotics launches new trailer pallet unloading solution

3. Full set of design files released for PR2 robot

2. Editor’s Picks: best robots of ProMat 2023

1. John Deere acquires SparkAI’s human-in-the-loop tech