Category: Resources

READY Robotics Releases Forge/OS 5, the World’s First Open Platform for Controlling Robots from Top Robot Brands

Forge/OS 5 allows manufacturers to deploy virtually any robot with their existing team, enables OEMs and machine builders to create solutions that work seamlessly with 250+ robots and countless peripherals, and empowers developers to build automation apps that instantly scale across the top robot brands

Columbus, OH -May 11, 2021- Today READY Robotics announces the launch of Forge/OS 5, the first open, cross-brand operating system for industrial automation. With support for FANUC, Yaskawa, ABB, Staubli, Epson and UR robots, Forge/OS 5 offers a single programming interface for hundreds of models of collaborative and industrial robots, as well as the peripherals required for automation. For the first time ever, Forge/OS 5’s open platform enables machine builders and software developers to create solutions and applications that instantly scale across robot brands.

Shaped by years of feedback from manufacturers ranging from Fortune 500 to small machine shops, Forge/OS 5 empowers companies to manage their own automation without needing deep expertise in robot programming. It also vastly reduces the complexity for those with multiple brands of robots and peripherals on their production floor, by abstracting away brand-specific robot programming languages, and making grippers and other peripheral hardware straightforward to integrate. Any robot powered by Forge/OS 5 can be easily programmed by operators by using Task Canvas, READY’s no-code programming app.

“For robotics to reach the scale we need as a society, we must make it easier for end users and we must make it easier for developers,” says Benjamin Gibbs, CEO and co-founder of READY Robotics. “As we’ve seen before with Windows and Android, software platforms can enable massive growth within an industry.”

Forge/OS 5 has been built from the ground up as a developer-friendly, open platform that facilitates third-party development. Software developers can build apps focused on a variety of use cases that scale across top robotic brands, benefitting from accessible core services of a modern, cross-brand robotic software platform. Hardware OEMs can easily integrate their hardware with any robot powered by Forge/OS 5. And custom machine builders can create solutions for customers without needing expertise in brand-specific robot programming languages. 

With a rapidly growing ecosystem of over 50 partners, composed of leading companies in industrial automation, Forge/OS 5 significantly simplifies the integration of hardware into automated workcells. “By unlocking access to robot programming and enabling out-of-the-box control of robotics and hardware, Forge/OS 5 enables hardware OEMs like SCHUNK to accelerate product development and adoption, and enhance robotic applications far beyond what was possible yesterday,” says Milton Guerry, President of SCHUNK USA.

“Until now, there hasn’t been a good channel for developers to build the apps that have the power to ignite the explosive spread of automation,” states Juan Aparicio, Vice President of Product at READY. “We are incredibly excited to enable the brightest builders in the world, regardless of background, to dream up and create products that will impact automation the way they have impacted other industries. Creating a true developer ecosystem for automation has the potential to usher in a new golden age of automation.”

About READY Robotics
Founded in 2016, READY Robotics created Forge/OS, the first enterprise-grade operating system that controls robot arms from many top robot manufacturers, as well as the peripherals needed to make those robots productive in a real world environment. Through enterprise-grade software (Forge/OS), the first marketplace for automation (READY.Market), and an online automation learning platform (READY.Academy), READY makes it dramatically easier for factories to deploy robotic automation. READY is based in Columbus, Ohio.

Getting Started with OLRP

At READY, we’re focused on eliminating the programming barriers that slow the spread of automation. Fragmentation and the difficulty of programming robots prevent many from deploying automation, and others from automating perfectly viable tasks. READY is attacking this in numerous ways: Forge/OS addresses fragmentation with a common operating system for top robot brands. Task Canvas provides a common, intuitive programming platform for top robot brands. READY Academy upskills workers on designing, deploying and managing automation. And, READY Market simplifies sourcing the components needed to automate. Simply put, there are many approaches to reducing the programming barrier and empowering manufacturers to deploy automation. One that pairs well with Forge/OS, is offline robot programming (OLRP) with OCTOPUZ! By enabling the programming of robots in an offline environment (on a computer), OLRP eliminates the need for robots to be taken off production. This greatly improves productivity and the bottom line. Our partner OCTOPUZ is leading the cross-brand OLRP movement, and their Head of Sales & Marketing Jon House outlines some considerations for those exploring OLRP. Article originally published on OCTOPUZ’ blog.

What is Offline Robot Programming (OLRP)?

If you’re new to OLRP, we have a couple of resources to get you familiar.

What are your industry options for OLRP? Who offers OLRP?

Once you decide that OLRP will provide value to you, where do you look? Here is a breakdown of your options:

  1. Robot Manufacturers (OEM) Solutions
    
  2. Agnostic OLRP
    1. Low-Cost solutions
    2. Mid-Range solutions 
    3. High-Cost solutions

OEM Solution

I.e. Roboguide for Fanuc. Motosim for Motoman. Etc.

These have their place, so you should look at them! Some things to consider.

  1. From a cost perspective, as a rule of thumb, these will be ~ 1/3rd the price of the Mid-Range Agnostic Solutions.
    
  2. The integration with the robot will be very tight with the OEM solution. For example, Motosim will have support for all the Motoman specific functions found on your controller. Also, Post Processors will require almost no customization. This software can also be considered a virtual teach pendant. Lastly, aligning the virtual world with the real world should be relatively seamless.
    
  3. They are brand specific. So, if you have multiple brands of robots, you will have to pay for, learn, and maintain multiple different software with varying capabilities.
    
  4. Certain OEM softwares are better than others, but in general, they will lack the usability and functionality found in the agnostic solutions. Why? I believe it is because the software is an afterthought for the Robot Manufacturers, as their focus is on the hardware. So, what problem are you trying to solve with an OLRP solution? And how important is it to fix that problem?
    
  5. How often do they improve their software with impactful new features? Ask them to show you. 

Agnostic OLRP

Some things to consider:

  1. Do you want to solve the entire problem or just part of the problem? The low-cost solution may be better than nothing, but it will likely not truly solve your problem. Why? The low-cost solution can likely not program paths on even semi-complex geometry. They likely do not have support for advanced functions, like error avoidance. They likely do not have a robust training and support program. So, do you want to fully solve the problem?
    
  2. Do you need 100% accurate cycle times? Or just something close enough? The high-cost solutions will likely have the RCS (Realistic Controller Simulation) modules built-in, which will give you spot on cycle times. 100% accurate cycle times are important for certain industries like automotive. But if you are in General Industry, this is likely not critical for you. The mid-range solution will provide you relatively accurate cycle times at a much lower price.
    
  3. The implementation process (calibrating the virtual world with the real world) is an extremely critical part of the solution. Don’t just trust that the pretty picture on the screen will translate flawlessly to the real world. Ask for customer references. OLRP is not very valuable if it is not driving the robot to the exact right coordinates in the real world. More on implementation here.

What to look for in a demo

The demonstration is the key meeting in the evaluation process. It’s where you get a feel for how the software flows and what it’s capable of. You try to envision yourself or your team using the software. Here are my recommendations for you, the potential customer, going into the demo stage.

  1. At least one demo must use your CAD. A real-world part that you are cutting. Don’t let the suitor getaway easy by demonstrating on their perfect part that they’ve demoed on 300 times before.
    
  2. The demo should at least mimic your real-world cell. Don’t let the suitor build a demo cell that is oversimplified and leaves out important real-world features. Why would you want to see a demo of a single robot cell when in reality you have a dual-robot cell with a rotary positioner?
    
  3. It should look easy, the interface should be nice, and all that stuff. But at the end of the day, the three most important aspects of the software for you should be, 1) How effectively can the software create the correct paths for your application? 2) How effectively can the software avoid errors (joint limits, collisions, singularities, etc.). And 3) How effectively can the software post out the correct program to my robot? Everything beyond those three items are just nice-to-haves. Go deep with evaluating how functional the software is on items 1, 2 & 3. Here are some questions:
    1. How do I create a path?
    2. Can I copy that path elsewhere?
    3. How do I go back in to edit that path?
    4. How do I edit multiple paths at the same time?
    5. How do I ensure my program is error-free?
    6. Can I import and program CAM paths on my robot?
    7. Can you show me what the finished code looks like?
    8. What’s the last feature you developed and pushed out to your customers related to 1, 2, or 3. When did you release it, and why did you develop it?


And some red flags to watch for:

  1. Be concerned if the vendor shrugs off the specifics related to your equipment because they are probably making a lot of assumptions that will undoubtedly result in major hiccups when implementing and supporting. The vendor should want to know quite a bit about your equipment (make & models, options, robot backups, expectations, etc.)
    
  2. Be concerned if the vendor does not want to offer a trial. It is free for the vendor to do this.
    
  3. Be concerned if the vendor does not want to offer references. Somebody who is using the software every single day to create programs.
    
  4. Be concerned if the vendor has not pushed out any impactful features in the last 3-5 months. 

A final thought to leave you with.

When I talk to our prospective customers, I remind them that:

  • The soft costs to deploy a piece of software are often 3x+ higher than the direct costs. The training, The business process change. The work to trial the software on top of your already existing duties. Etc. Etc.
    
  • The time investment cost is high. If the software fails to work as expected, the customer loses all that time invested in evaluating, trialing, and deploying the software.
    
  • Picking the wrong vendor can be a huge mistake in terms of time + productivity. It’s bad enough if the implementation fails. But what if you’d picked the “right” vendor instead, and it actually worked right away. That could have saved you a year or more. A year or more is a long, important amount of time…

READY Robotics Announces Juan Aparicio as VP of Product

Columbus, OH – March 3, 2021 – Today READY announces two key hires, bringing in proven automation leaders to head up product and channel partner efforts. Juan Aparicio joins READY as VP of Product after a decorated career at Siemens Technology where he was Head of Advanced Manufacturing Automation. And, joining READY as Director of Business Development, Mark Patterson comes from Schunk, where he built out their collaborative robotic sales channels. These hires reflect READY’s commitment to building Forge/OS into a robotics and automation platform that impacts manufacturing and automation at a broad scale.

“I couldn’t be more excited to add Juan and Mark to the READY team! Juan’s impact at Siemens, and on the greater automation community, cannot be overstated. His expertise will be invaluable in refining Forge/OS into a platform that can unlock automation and ignite innovation.” Ben Gibbs, READY CEO and Co-Founder, adds, “Of course, getting automation technology into the hands of the market is about more than building a great product, which is why I’m equally excited to add Mark Patterson to lead channel partner development. Mark’s experience building out the collaborative robotics sales channels for Schunk will enable READY to build a powerful distribution network that makes Forge/OS-powered automation accessible to all manufacturers.”

Juan Aparicio joins READY after over a decade at Siemens. While leading the advanced manufacturing automation team at Siemens, Aparicio was awarded MIT Tech Review Innovator Under 35 Europe, granted numerous patents, and recognized as Siemens’ 2020 Inventor of the Year. His work has been featured at the New York Times, MIT Tech Review, Wired, Forbes, and other media outlets. Aparicio is a member of the Technical Advisory Committee for the Advanced Robotics in Manufacturing (ARM) Institute, a member of the A3 Strategic Advisory Committee on AI, and a startup advisor at UC Berkeley’s Skydeck. In his role as VP of Product, Aparicio will guide all product strategy, with a focus on advancing Forge/OS into a widely-adopted, cross-brand robotics and automation platform that exponentially accelerates the distribution of industrial automation. 

“When I joined Siemens in 2010, I couldn’t have dreamt to have such an impactful career. It’s been the honor of a lifetime. As I look into the future, the opportunity to lead Product for READY is impossible to pass up.” says Aparicio. “My personal mission is to democratize access to robots and unleash the power of automation, and there is no better time and place to do it than right now at READY. The field of robotics is on the verge of a Cambrian explosion, in terms of the amount of robots and applications where they will be deployed. I look forward to working with the READY team to help make this a reality!“

Joining READY to lead channel partner development efforts is Mark Patterson. Patterson comes from Schunk, where he built their collaborative robotics sales distribution channel from the ground up. “It’s exciting to help READY smash the barrier that prevents robot adoption. With their established end-user relationships and automation expertise, distributors and integrators will play a critical role in getting Forge/OS-powered automation in the hands of manufacturers. I’m eager to build a network of trusted partners that believe in the value of Forge/OS as much as I do!” 

About READY Robotics
Founded in 2016, READY Robotics created Forge/OS, the first enterprise-grade operating system that controls robot arms from many top robot manufacturers, as well as the peripherals needed to make those robots productive in a real world environment. Through enterprise-grade software (Forge/OS), the first marketplace for automation (READY.Market), and an online automation learning platform (READY.Academy), READY makes it dramatically easier for factories to deploy robotic automation. READY is based in Columbus, Ohio.

READY Academy Introduces Certifications

Author: Christie Opiekun

In October we launched READY Academy, an online educational platform where manufacturing professionals can learn to design, deploy, manage and scale automation. We built READY Academy because we view education as a key component to unlocking automation for a wider array of manufacturers. Now, we’re excited to add certification programs to READY Academy, enabling students to earn the designation of READY Certified Robot Programmer.

In addition to new skills that will increase their value on the manufacturing floor, READY Academy certification courses give students verification for the skills they have acquired. The new certification courses also provide employers and educational institutions with highly structured curriculum for workers and students.

Taught by instructors with PhDs in robotics, the newly released certification courses educate students on robot kinematics, the basics of programming a robot, the Forge/OS interface, programming a robot to perform a machine tending task, safety devices, and risk assessments. Upon completion of these courses, students are awarded a certificate recognizing them as READY certified robot programmers.

There are presently three certification courses, with more in development:

Certification: Fundamentals of Robot Programming with Forge/OS

In this intensive course, Kel Guerin, PhD guides you through every step in using Forge/OS to program an industrial robot. From understanding the basics of robot kinematics, to setting up tooling and peripherals, to using the features of Forge/OS, by the end of this course, you will have all of the tools necessary to build and program complex tasks with an industrial robot.

View Course

Certification: Fundamentals of Machine Tending

In this course, Kel Guerin, PhD teaches you the basics and best practices for building out machine tending tasks. You will walk you through the steps of mill and lathe tending, learn techniques for ensuring your task is reliable, and discover proven methods for optimizing your task for maximum efficiency.

View Course

Certification: Principles of Robot Safety in Industrial Automation

In this course, Jake Huckaby, PhD introduces the topic of robot safety. You will learn about some basic safety devices, techniques used in improving workcell safety, and the importance of risk assessments. By the end of the course, you will be able to identify important safety considerations when designing, programming, and running your robot workcell.

View Course

Supervised Learning is Critical to the Future of Automation

Robots will continue to get smarter, but sometimes they will need to learn from the expert…us

Author: Kel Guerin, PhD, Co-Founder and CIO, READY Robotics

The primary goal of automation is to free up people to be more productive. That means that instead of a person doing a dull or repetitive task, a robotic system can be used. This allows the person to accomplish more by focusing on tasks that require more thought, creativity, and problem-solving – tasks that ultimately generate more value. When a robotic system is able to accomplish its task with some amount of autonomy, for instance, picking from a bin of randomly placed parts rather than requiring neat stacks of parts, people are liberated even more. However, robots still don’t get everything right. For instance, the best algorithms for vision, that let a robot see and interact with its environment, are still not 100% accurate. 

These algorithms for reproducing a task, whether it is detecting an object or learning to hold a metal part, learn by repeating the same action over and over again. The more they do the action, the better they get. However, this only works when the task never changes, when the parts don’t vary, or when the parts are presented in the same location and orientation. When any single aspect of a task varies, the algorithm may not be able to cope with the change in circumstances.

Mentorship of a Robot

In order to improve this performance, and deal with these new situations, there is a strategy employed by many algorithms called supervised learning. Since many of these robots are deployed in environments where the people that used to perform the robot’s task are still nearby, the idea behind supervised learning is if the robot encounters a situation where it does not know how to perform because of some condition in the environment it has never seen before – a part it has never seen, two parts stuck together, a jam in the machine, etc. – a person can provide the missing information the robot needs. That information might be in the form of a new label (yes, that is still a bottle of soap, even though it’s upside down) or a new demonstration (you need to pick up this new part here). The user demonstrates, provides the additional information, and the robot is back to work. 

This type of corrective assistance that the person is providing to the robot is also common in how people transfer skills to each other. In many different trades, this type of mentorship has happened for thousands of years and is still a common way new workers are taught (as opposed to, or in addition to a classroom setting). There are even companies that are using AI to better capture this mentorship. 

So do people have time to do this robot mentorship? Well, by freeing up people from repetitive tasks with automation, we inadvertently create a resource of knowledgeable workers, already present alongside the robotic system to perform that mentorship in order to improve performance. Imagine a worker who previously spent all their time working at a single machine, who now manages a team of robots, occasionally tweaking each robot’s performance by providing that extra information from their own know-how. They are more empowered because their productivity is higher, they are freed up from performing an undesirable task themselves, and they are still using their considerable knowledge to continuously improve the robotic systems through mentorship.

Usability is the Key

A key element of designing a robotic system that can effectively be trained by a worker through supervised learning is ensuring that the interface the worker uses to provide that extra information is easy to use. This is especially true if those performing supervised learning are machine operators (which are common) and not robotics engineers (which are exceedingly rare).

If we want to leverage the fact that people are familiar with (and good at) mentorship and apply that to making automated systems better, then the interfaces used to provide that information to the robot need to be similar to how they train other people. For instance, it is often the case that workers demonstrate motions in order to teach a skill. You need to hold the tool like this, and press here. With the availability of “collaborative robots”, people can now demonstrate a motion directly, by moving the arm of the robot. Additionally, we are starting to see vision systems that can watch the motions a person makes, such as painting with a paint sprayer, and use that demonstration to define the motions of the robot. This works well because again, it is very similar to how people demonstrate motions to each other. Teaching the robot in this way is natural, and it enables even those without extensive robotics experience to train robots. This is key to enabling more rapid adoption of automation.

From a learning perspective, a worker might need to show (at least once) how a task is done so the robot can learn from that. The worker defines a set of steps to complete a task and represents those steps in some logical flow. Workers commonly use flow charts to teach or communicate with other workers how a process works step by step. There is now No Code software that provides this same flow chart interface for programming the robot. This means that instead of learning to code, a worker can learn a conversational interface based on something they are already familiar with, with significantly less effort.

No Code software with its significantly decreased learning curve also makes learning about robots easier. The educational barrier to robotics is very high, and much of that stems from the time required to learn to program in the robot’s native programming language. Instead, when programming is rapidly learned, those precious hours of upskilling can be geared toward learning more about the best way the robot can do the task. How does the robot best hold parts? How does the robot deal with variability? How is the robot most likely to fail at the task? The more the worker can get inside the robot’s “head”, the more they can help guide that robot to success. 

Adapting to New Situations

A key piece of a robot’s ability to learn is when that knowledge can be used in a new situation. 

Now, when we are talking about the robot’s ability to learn, we are really talking about the software or algorithm’s ability to learn. This means that for this type of learning to be truly useful, once a skill is learned, it should work no matter what robot – or hardware – is doing the task. This is a simple idea, but it seldom works like this in practice. 

For example, let’s say an algorithm learned to pick cylinders from a bin with a small industrial robot with a vacuum gripper. It knows what the part looks like, and where to place the vacuum cup so the part is held securely. Now, the user wants to use a larger robot with a two-finger gripper. The algorithm still knows how to manipulate the cylinder, but it needs new information about this bigger arm, and where to position the two-finger gripper for the best grasp. This process of learning the fundamentals of a skill, agnostic of the particular hardware, is called abstraction, and people do it all the time. Hand a skilled carpenter five different hammers and they will still be able to hit a nail because they know the task beyond the constraint of a tool. 

So now imagine a machine operator who is in charge of multiple such robots. They must provide this additional information to allow the robot to grab the part under these new circumstances. However, this operator must deal with many such machines and situations, potentially with robots from different brands. It is essential that this user has a consistent interface to provide this extra information, meaning the robots must be running the same overlaying software. This common software layer also benefits the algorithm, since the algorithm now a standardized interface to the robot, and a representation of that robot that is agnostic of its size and configuration. After all, the robot can still move and grab, it just happens to be larger and have a different hand. 

There have recently been technological advances that allow for a learning algorithm such as the one described to work on any brand of robot because they run on a common underlying software platform. Think Windows for PCs – one algorithm can run on many different computers, and all the underlying software just works. In this situation, this common platform makes every robot look the same in the eyes of the algorithm, making the translation of skills between robots much easier.

Conclusion

Robots that can figure things out on their own still have a lot to learn from us, and supervised learning is a way to impart that knowledge. However, in order to truly enable supervised learning, systems need to be usable by the people that are providing that extra knowledge. User interfaces need to be similar to how people already teach each other, and easy to use so that it is as natural to show a robot the ropes as it is to show another person. Additionally, common software platforms for robot hardware can enable supervised learning too much more quickly abstract to new situations because the robot and tools appear the same to the algorithm, even though they may be of different sizes and configurations. Put it all together, and with the right architecture for supervised learning (conversational, intuitive robot programming interface, a common interface across robot models and brands, and software optimized for supervised learning input and teaching) robots don’t just become more effective, they are able to handle tasks that would have been very difficult to program, and the machine operators using the robots are fully leveraged – enabling previously impossible levels of productivity.

Does Manufacturing Need a New Term for Robotic Automation?

Robotic Automation has become an overused phrase because of an explosion in the software automation of business systems. Robotic Automation is a shortened form of Robotic Process Automation, RPA, that describes a process that is used to automate existing software applications. The problem is, Robotic Automation is also a term used in Advanced Manufacturing to describe the automation of manufacturing systems using industrial and collaborative robots.

But what is RPA exactly, and why is it different from the use of robotics in manufacturing? 

RPA is a technology that allows almost anyone to configure a software “robot” to emulate the actions of a user working with business software. RPA robots use the business applications just like humans do. RPA robots primarily automate repetitive tasks, and through that automation improve the speed of processing and the quality of work. An example of this would be an RPA “robot” opening a software application, copying specific data sets, then pasting the data into a separate database at predetermined intervals. Humans can then focus their time on tasks that require in-depth analysis rather than the application of wrote rules in a repetitive fashion. RPA allows organizations to automate their business processes at a fraction of the cost and time from implementing alternative software solutions. RPA leverages the existing application infrastructure without causing disruption to underlying systems, which would be difficult and costly to replace.

RPA differs from industrial automation of robots since Robotic Automation in the context of advanced manufacturing is the use of industrial or collaborative robot arms to automate a production process.

This raises a question – does manufacturing need it’s own distinct term for robotic automation? Like hashtags in social media, a unique term for robotic automation in manufacturing would make relevant content more discoverable for manufacturers seeking information on industrial automaton. As robotic automation continues to spread throughout the manufacturing world in parallel with advanced manufacturing, a distinct term for the sector would provide more and more value over time. So what term might be logical to describe the process of robotic automation in a manufacturing setting? Perhaps “robotomation” is the new noun that manufacturing needs. 

ro·bot·to·ma·tion

/ˈrōˌbät,əˈmāSH(ə)n/

noun

noun: robotomation; plural noun: robotomations

hashtag

#robotomation

the use of an industrial or collaborative robot used to automate a manufacturing or other production process.

Example Usage:


“The elimination of dull, dirty and dangerous jobs in manufacturing through the spread of robotomation.”

“The elimination of the manufacturing skills gap through the use of easier to use, more standard, robotomation implementation options”

Origin

English
Robot

— Robotomation

English
Automation

2020 (originally US): irregular formation from robot + automation.

First came into use in discussions between Aaron Prather, R&D Evangelist for FedEx Express, Kathy Walker, Founder and CEO of the eKentucky Advanced Manufacturing Institute (eKAMI), and Alessandra Walker, Inside Sales Executive at Heartland Automation LLC.

Extended Definition

Robotomation is the application of industrial or collaborative robots to automate manufacturing processes in a cost effective manner.  Robotomation depends on easy to use, standardized robot programming languages such as that developed by READY Robotics.  READY robotomation is implemented using Forge/OS, the world’s first universal operating system for robots with the easiest to use, cross brand, robot agnostic programming language.  READY robotics provides courses in robotomation through their READY.Academy and a marketplace of robotomation solutions at READY.Market.

Press Release: READY.Market Launches with 15 Partners

READY Robotics launches READY.Market, a robot agnostic online marketplace for manufacturers looking to accelerate their implementation of robotic automation.

READY.Market is a single resource manufacturers can use to research complete robotic automation solutions. Through extensive documentation, how-to and training videos at the READY.Academy, and detailed product listings, manufacturers can identify and select the components and suppliers needed to solve their manufacturing challenges.

COLUMBUS, OH – December 1, 2020 – Selecting the components needed to deploy automation can be a major barrier to robotic automation. To make automation more accessible to all manufacturers, READY Robotics has created a marketplace in partnership with leading providers of automation products. By enabling manufacturers to find the components needed to automate, the READY.Market empowers manufacturers to deploy automation on their own.

READY.Market, featuring products compatible with READY’s Forge/OS software, rapidly accelerates the implementation of complete automation solutions using robot arms from manufacturers such as Yaskawa, FANUC, and Universal Robots. The announcement of READY.Market is the first step in building a robust ecosystem for easy-to-use robotic solutions that work with any robot arm. 15 partners are live on READY.Market today, and READY will continue to introduce complete robotic solutions, add additional partner products, and services through READY.Market.

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“Complete solutions for robotic automation can reduce the overhead of automating,  but they have typically been isolated to individual robot brands,” says Chris Swaim, Director of Strategic Partnerships at READY Robotics. “Forge/OS, provides an easy-to-use interface that unlocks the ability for end users and partners to access the full range of robot brands, sizes, and speeds. The result is that manufacturers can use the right robot for the right job when paired with READY’s Forge/OS software and partner products known to work together.  READY.Market simplifies the selecting of components and full-blown automation solutions.”

“Schunk is excited to bring our extensive portfolio of grippers, clamping, and workholding solutions to READY.Market,” says Cory Raizor, Sales Account Manager at Schunk. “READY’s Forge/OS software pairs well with our comprehensive product line allowing the customer to introduce any size of robot to the manufacturing floor.” 

Forge/OS is available today, and enables a partner ecosystem of compatible products that allow manufacturers to be more agile than ever using solutions that are easy to use, lower cost and faster to implement than traditional solutions. To learn more visit READY.Market, or for partnerships contact partnerships@ready-robotics.com.

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About READY Robotics

Founded in 2016, READY Robotics created Forge/OS, the first enterprise-grade operating system that controls robot arms from many top robot manufacturers, as well as the peripherals needed to make those robots productive in a real world environment. Through enterprise-grade software (Forge/OS), the first marketplace for automation (READY.Market), and an online automation learning platform (READY.Academy), READY makes it dramatically easier for factories to deploy robotic automation. READY is based in Columbus, Ohio.

Related Links

Contact

Erik Bjørnard

VP Marketing, READY Robotics

erik.bjornard@ready-robotics.com

614.264.4936

READY Robotics Named a Cool Vendor in Gartner’s September 2020 Cool Vendor in Supply Chain Execution Technologies

On September 30, 2020 Gartner Research announced their Gartner Cool Vendors, selecting READY Robotics as a Cool Vendor in Supply Chain Execution Technologies. According to Gartner, “Supply chain technology leaders should use Cool Vendors in Supply Chain Execution Technologies research to identify emerging supply chain execution technology vendors that can drive enhanced business value”. 

Industrial robots and automation can increase the productivity of human workers through operational efficiencies. By simplifying robot programming, and providing a common programming platform across robotic brands, READY solves two barriers that limit the spread of robotic automation: a high fragmented industrial robot market, and complex, proprietary robot programming languages. By democratizing the programming of industrial robots, READY is helping to make automation more accessible, and increasing productivity across the industry. 

According to Gartner Research, “71% of CEOs said that hiring and people development would slow in an economic downturn. This places extreme pressure on organizations to increase productivity directly by making workers more efficient or indirectly by supplementing human capital with automation.”

As we have seen, the global pandemic has illuminated weaknesses in global manufacturing supply chains that can be addressed through automation. Some of these soft spots include: production that is heavily concentrated geographically, limited local suppliers and production capability, and sensitivity to shifts in worker availability. Through increased automation, manufacturers can address these factors and develop strong, diversified supply chains. Of equal importance, automation will enable manufacturing to simultaneously overcome the supply chain shortcomings that have been magnified by the current pandemic, and solve the ongoing skilled labor shortage that has constrained manufacturing. Automation has never been more important, and READY Robotics is proud to play a role in helping enterprise manufacturers as well as machine shops increase efficiency, and boost production with their existing workforce.

By making robots easy to program, with a common programming interface that works across many of the top robot OEMs, READY Robotics is making automation more accessible at a time when automation is increasingly important. READY Robotics has built Forge/OS to address a common challenge in industrial automation, technology that is far too difficult to use by all but the most specialized workers. READY Robotics is honored to be named a Gartner Cool Vendor, and we’re excited to continue empowering manufacturers of all shapes and sizes to boost productivity by deploying automation that their existing team will own.

Supply chain leaders can look to the report to understand the increasing importance of digital solutions in supply chain management, and read Gartner’s recommendations for executives as they evaluate new technologies. 

Gartner, “Cool Vendors in Supply Chain Execution Technologies”, Dwight Klappich, Bart De Muynck, Carly West, Simon Tunstall, 30 September 2020.

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