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Top 10 ROS-based robotics companies in 2019

Top 10 ROS-based robotics companies in 2019

Source: Ricardo Tellez

The Robot Operating System is becoming the standard in robotics, not only for robotics research, but also for robotics companies that build and sell robots. In this article, I offer a list of the top 10 robotics companies worldwide that base their robotics products on ROS.

Criteria

This is the list of criteria I followed to select the winners:

  • We are talking about robotics companies that build robots. This is not about companies that produce some kind of software based in ROS, but companies that create and ship robots based in ROS. We do not consider companies that do consulting and generate solutions for a third party, either.
  • They have created the robots themselves. This means they are not resellers or distributors of robots made by somebody else.
  • They have their robots natively running ROS. This means, you switch the robot on, and it is running ROS. We are not taking into account robots that support ROS — if you install the packages. We concentrate on robots that run ROS off the shelf. For example, you can run ROS on a UR5 arm robot, but if you buy the UR5 robot, it will not come with ROS support off the shelf. You need to add an extra layer of work. We are not considering those robots.
  • You can program the robots. Even if some companies provide ROS-based robots — such as Locus Robotics — they do not provide a way to program them. They provide the robots as a closed solution. We are not considering closed solutions here.

To summarize the criteria: 1. You can buy the robot directly from the company; 2. The robot runs ROS from Minute 1; and 3. You can program the robot at will.

Once the companies were selected based on the previous criteria, I had to decide the order. Order was based on my personal perception of the impact those companies are making in the ROS world. That is very subjective to my own experience, I know, but that is what it is. Whenever I felt it necessary, I described my motivation behind the position of the company on the list.

Now, having clarified all that, let’s go to the list!

Top 10 ROS companies

1. Clearpath Robotics

Clearpath is a Canadian company founded in 2009. The number of robots that it produces in the fields of unmanned ground vehicles, unmanned surface vehicles (on the water), and industrial vehicles is amazing. The company’s robots are based on ROS and can be programmed with ROS from Minute 1. That is why these robots are used in the creation of third-party applications for mining, survey, inspection, agriculture, and material handling.

Some of Clearpath’s best-known robots include Jackal UGV, which you can learn how to program. Others include the Husky UGV, Heron USV, and its recently launched series of Otto robots for industrial environments.

As a matter of trustability, this company took the responsibility to provide the customer support to the existing PR2 robots, once Willow Garage closed its doors. Because of that, and because it is the company with the most varied ROS robots available, I put it in the well-deserved No. 1 spot on this list.

I interviewed Ryan Gariepy, CTO of Clearpath, for the ROS Developers podcast. You can listen to the interview here.

2. Fetch Robotics

Fetch Robotics was founded by Melonee Wise in 2014, after she was forced to close her previous pioneer company, Unbounded Robotics. We can say that Fetch has two lines of business. First is its line of mobile manipulators, which are mainly used for robotics research.

Then, Fetch has a line of industrial robots which it sells in fleets ready to be deployed in a warehouse to help with the transport of materials. As I understand it, the first line of business is the only one that allows direct ROS programming, and the second one is a closed product.

I did not select Fetch for No. 2 because of its research line only. I selected it for this spot because Fetch was a pioneer in the creation of affordable mobile manipulators with its Fetch robot (paired with the Freight mobile platform). Up to the moment it released Fetch, there was no ROS-based mobile manipulator on the market. (Sorry, Turtlebot 2 with a Dynamixel arm doesn’t count as a mobile manipulator.)

Recently, Fetch organized the FetchIt! challenge at ICRA 2019. (My company, The Construct, was a partner contributing to the event’s simulation.) At that event, participants had to program their Fetch to produce some pieces in a manufacturing room. You can check the results here.

Even if Fetch Robotics only produces two robots meeting the criteria above, it was the pioneer that opened the field of ROS-based mobile manipulators. That is why it deserves the No. 2 spot on this list.

I interviewed Melonee Wise, CEO of Fetch Robotics, for the ROS Developers podcast. You can listen to the interview here.

3. Pal Robotics

Pal Robotics is based in Barcelona and was created in 2004. I especially love Pal because I worked there for more than seven years, and many of my friends are there. But love is not the reason I put them in the third position.

Pal Robotics earned No. 3 because it’s the only company in the world that builds and sells human-size humanoid robots. And not just a single type of humanoid, but three different types! The Reem robot, Reem-C robot, and recently, the TALOS robot.

Pal also produces mobile manipulators similar to the Fetch ones. They are called Tiago, and you can buy them for your research or applications on top. (If you’re interested, you can learn how to program Tiago robots with ROS in an online course that The Construct created in collaboration with Pal Robotics.)

We have recently released a simulation of TALOS, including its walking controllers. You can get it here.

I interviewed Luca Marchionni, CTO of Pal Robotics, for the ROS Developers podcast. You can listen to the interview here. Also, you can learn what is catkin_make and how to use it.

In addition, I interviewed Victor Lopez, main DevOps engineer of Pal Robotics, for the ROS Developers podcast. You can listen to that interview here.

4. Robotnik

Robotnik is another Spanish company, based in Castellon and founded in 2002. I call it “the Spanish Clearpath.” Really, it has built as many ROS robots as the first company on this list. Robotnik creates and designs mobile manipulators, unmanned ground vehicles of different types, and many types of mobile robots for industrial applications and logistics.

The company is also expert in personalizing your robot by integrating third-party robotics parts into a final ROS-based robot that meets your requirements.

Finally, Robotnik’s team includes the people behind the ROS Components online shop, where you can buy components for your robots that are certified to be ROS supported off the shelf. For all this extensive activity in selling ROS robots, Robotnik deserves the fourth position on this list.

A couple of months ago, Robotnik sent us one of its Summit XL robots for experimenting and creating ROS training materials. We used it extensively for our ROS Live Classes, showing how to program Robotinik robots using a cloud robotics platform.

We also created a specific course to train people to program their Summit XL robot.

I interviewed Roberto Martinez, CEO of Robotnik, for the ROS Developers podcast. You can listen to the interview here.

5. Yujin Robots

Yujin is a Korean company specializing in vacuum cleaning robots. However, those robots are not the reason they are on this list, since they do not run ROS onboard. Instead, Yujin is here because it’s the official seller of the Kobuki robot, that is, the base system of the Turtlebot 2 robot.

The Turtlebot 2 is the most famous ROS robot in the world, even more so than the PR2! Almost every one of us has learned with that robot, either in simulation or in reality. Due to its low cost, it allows you to easily enter into the ROS world.

If you have bought a Turtlebot 2 robot, it is very likely that the base was made by Yujin. We used Kobuki as the base of our robot Barista, and I use several of them at my ROS class at La Salle University.

Additionally, Yujin has developed another ROS robot for logistics that is called GoCart, a very interesting robot for logistics inside buildings (but not warehouses). The robot can be used to send packages from one location in the building to another — including elevators on the path.

6. Robotis

This is another Korean company that is making it big in the ROS world. Even if Robotis is well known for its Dynamixel servos, it’s best known in the ROS world because of its Turtlebot 3 robot and Open manipulator, both presented as the next generation of the Turtlebot series.

With the development of the Turtlebot 3, Robotis brought the Turtlebot concept to another level, allowing people easier entry into ROS. The manipulator is also very well integrated with the Turtlebot 3, so you can have a complete mobile manipulator for a few hundred dollars.

Even easier, the company has made all the designs of both robots open-source, so you can build the robots yourself. Here are the designs of Turtlebot 3. Here are the designs of Open Manipulator.

7. Shadow Robot

Shadow Robot is based in London. This company is a pioneer in the development of humanoid robotic hands. To my knowledge, Shadow Robot is the only company in the world that sells that kind of robotic hand.

Furthermore, its hands are ROS-programmable off the shelf. Apart from hands, Shadow Robot also produces many other types of grippers, which can be mounted on robotic arms to create complete grasping solutions.

One of its solutions combined with third-party robots was the Smart Grasping System released in 2016. It compbined a three-fingered gripper with a UR5 robot. Hhere is a simulation we created of the Smart Grasping System, in collaboration with Ugo Cupcic.

Shadow Robot’s products include the Shadow Hand, the Cyberglobe, and the Tactile telerobot.

Demonstrating its leadership in the field, Shadow Robot’s hands were selected by the OpenAI company to do their reinforcement learning experiments with robots that need to learn dexterity.

8. Husarion

Husarion is a Polish company founded in 2013. It sells simple and compact autonomous mobile robots called ROSbots. They are small, four-wheeled robots equipped with a lidar, camera, and a point cloud device. These robots are perfect for learning ROS with a real robot, or for doing research and learning with a more compact robot than the Turtlebot 2.

Husarion also produces the Panther robot, which is more oriented to outdoor environments, but with the same purpose of research and learning.

What makes Husarion different from other companies selling ROS robots is the compactness of its robots and its creation of the Husarnet network, which connects the robots through the cloud and has remote control over them.

I interviewed Dominik Novak, CEO of Husarion for the ROS Developers podcast. You can listen to the interview here.

9. Neobotix

Neobotix is a manufacturer of mobile robots and robot systems in general. It provides robots and manipulators for a wide range of industrial applications, especially in the sector of transporting material.

Neobotix is a spin-off of the Fraunhofer Institute in Stuttgart, and it created the famous Care-O-Bot, used many times in the Robocup@Home competitions. However, as far as I know, the Care-O-Bot never reached the point of product, even if you can order five of them and get them delivered, running immediately after unpacking.

At present, Neobotix is focusing on selling mobile bases, which can be customized with robotics arms, converting the whole system in a custom mobile manipulator.

The company also sells the mobile bases and the manipulators separately. Examples of mobile bases include Neobotix’s MP series of robots. On the mobile manipulator side, it sells the MM series. All of them work off-the-shelf with ROS.

Even if Neobotix’s products are full products on their own, I see them more as components that we can use for building more complex robots, allowing us to save time creating all the parts. That is why I have decided to put it in the ninth position and not above the other products.

10. Gaitech

Gaitech is a Chinese company that is mainly dedicated to distributing ROS robots, and ROS products in general, in China. from third-party companies. They include many of the companies on this list, including Fetch, Pal, and Robotnik.

However, Gaitech has also developed its own line of robots. For example, the Gapter drone is the only drone I’m aware of that works with ROS off the shelf.

Even if Gaitech’s robots are not very popular in the ROS circuit, I have included them it because at present, it’s the only company in the world that is building ROS–based drones. (Erle Robotics did ROS-based drones in the past, but as far as I know, that ceased when it switched to Acutronic Robotics.) Due to this lack of competition, I think Gaitech deserves the No. 10 position.

I interviewed May Zheng, VP of Marketing of Gaitech, for the ROS Developers podcast. You can listen to the interview here.

Honorable mentions

The following is a shortlist of other companies building ROS robots that did not make it onto the list for certain reasons. They may be here next year!

1. Sony

Sony is a complete newcomer to the world of ROS robots, but it has entered through the big door. Last year, it announced the release of the Aibo robot dog, which fully works on ROS. That was a big surprise to all of us, especially since Sony abandoned the Aibo project back in 2005.

Sony’s revived robot dog could have put it on the list above, except for the fact that the robot is still too new and can only be bought in the U.S. and Japan. Furthermore, the robot still has a very limited programming SDK, so you can barely program it.

If you are interested in the inner workings of Aibo with ROS, have a look at the presentation by Tomoya Fujita, one of the engineers of the project, during the ROS Developers Conference 2019, where he explained the communication mechanism between processes that they had to develop for ROS in order to reduce battery consumption in Aibo. Amazing stuff, fully compatible with ROS nodes and using the standard communication protocol!

2. Ubiquity Robotics

This is a company based on selling simple mobile bases based on ROS for the development of third-party solutions, or as it calls them, “robot applications.” Ubiquity Robotics’ goal is to provide a solid mobile base with off-the-shelf navigation on top of which you can build other solutions like telepresence, robotic waiters, and so on.

Ubiquity Robotics is a young company with a good idea in mind, but it’s very close to existing solutions like Neobotix or Robotnik. Let’s see next year how they have evolved.

I interviewed David Crawley, CEO of Ubiquity, for the ROS Developers podcast. You can listen to the interview here.

3. Acutronic Robotics

This company started building ROS-based drones, but recently, they changed direction to produce hardware ROS microchips. Acutronic produces the MARA robot, an industrial arm based on ROS2 on the H-ROS microchips.

However, as far as I know, the MARA robot is not Acutronics’ main business, since the company created it and sells it as an example of what can be done with H-ROS. That is why I decided not to include this company in the main top 10 list.

By the way, we also collaborated with Acutronic to create a series of videos about how to learn ROS2 using their MARA robot.

I interviewed Victor Mayoral, CEO of Acutronic, for the ROS Developers podcast. You can listen to the interview here.

ROS conclusions

Most of the ROS-based robotics companies are based on wheeled robots. A few exceptions are the humanoid robots of Pal Robotics, the drones of Gaitech, the robotic hands from Shadow Robots, and the robot arms from Neobotix.

It’s very interesting that we see almost no drones and no robotic arms running ROS off the shelf, since both of them are very basic types of robots. There are many robotic arm companies that provide ROS drivers for their robots and many packages for their control, like Universal Robots or Kinova.

But of the listed companies, only Neobotix actually provides an off-the-shelf arm robot with its MM series. I think there is a lot of market space for new ROS-based drones and robotic arms. Take note of that, entrepreneurs of the world!

Finally, I would like to acknowledge that I do not know all the ROS companies out there. Even if I have done my research to create this article, I may have missed some companies worth mentioning. Let me know if you know of or have a company that sells ROS robots and should be on this list, so I can update it and correct any mistakes.

Ricardo Tellez

About the author

Ricardo Tellez is co-founder and CEO of The Construct. Prior to this role, he was a postdoctoral researcher at the Robotics Institute of the Spanish Research Council. Tellez worked for more than seven years at Pal Robotics developing humanoid robots, including its navigation system and reasoning engine. He holds a Ph.D. in artificial intelligence and aims to create robots that really understand what they are doing. Tellez spoke at the 2019 Robotics Summit & Expo in Boston.

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Top 10 robotics stories during 1st half of 2019



We’re more than halfway through 2019, and there’s been a lot to talk about. Here are The Robot Report‘s picks for the top 10 robotics stories during the first half of 2019. Please share your thoughts below via the survey or the comments section.


Anki
Anki Cozmo robot. | Credit: Anki


1. Consumer robotics company Anki shuts down


The struggles of consumer robotics companies are well documented – see Jibo, Keecker, Laundroid, Mayfield Robotics – but it still came as a major blow to the industry when Anki shut down on April 29.


Anki raised more than $200 million since it was founded in 2010 and claimed it had revenue of nearly $100 million in revenue in 2017. And according to Anki Co-Founder and CEO Boris Sofman, who was hired by Waymo to lead its autonomous trucking efforts, the company “shipped over 3.5 million devices and robots around the world.”


Anki’s intellectual property is controlled by Silicon Valley Bank, which has had a security interest in Anki’s copyrights, patents and trademarks since March 30, 2018. Sources told The Robot Report that Anki already had a prototype of its next consumer robot. Anki also had a strategic partnership in place that “fell through at the last minute,” according to a former Anki employee.








2. Boston Dynamics enters logistics market


Another major surprise occurred April 2 when Boston Dynamics acquired Kinema Systems, a Menlo Park, Calif.-based startup that uses vision sensors and deep-learning software to help robots manipulate boxes. Essentially, this was Boston Dynamics’ entrance into the logistics market.


This is another sign of Boston Dynamics being more application-concious since it was acquired by SoftBank in mid-2017. The development of Handle and SpotMini, and the Kinema acquisition, point directly to that.


“I think Google planted the seed,” said Marc Raibert, CEO and Founder of Boston Dynamics. “And all of the other robotics companies near us were much more focused on applications and product than we were. So we’ve been turning that corner. It’s been a consistent thing. It’s not like we got to SoftBank and they hit us with a hammer and suddenly said, ‘make products.’ They’ve been extremely enthusiastic about our R&D work, too. It feels good to do both.”


Robust AI building commonsense toolbox for robots
Robust AI Co-Founders (left to right) Rodney Brooks, Mohamed Amer, Anthony Jules, Henrik Christensen and Gary Marcus at the Robust AI office in Palo Alto, Calif. | Credit: Peter Barret, Playground Global


3. Robust AI wants to give robots common sense


Giving robots the ability to think with common sense is a lofty goal, but an all-star team at Robust AI is trying to do just that. The Palo Alto, Calif.-based startup was announced by co-founder Henrik Christensen during his keynote at the Robotics Summit & Expo, produced by The Robot Report. The company has office space at Playground Global, its main investor, for the next 12 months.


Robust AI is trying to build an industrial-grade cognitive platform for robots. The company’s argument is that deep learning alone is enough to move the needle. To build its cognitive platform, Robust AI will take a hybrid approach by combining multiple techniques, including deep learning and symbolic AI, which was the dominant paradigm of AI research from the mid-1950s until the late 1980s.








4. Amazon launches new logistics robots


Kiva Systems, now known as Amazon Robotics after it was acquired by Amazon for $775 million in 2012, essentially created the mobile logistics robotics market we know today. The so-called Amazon effect prompted other startups to develop and offer automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) to retailers and third-party logistics (3PL) companies.


It’s major news when Amazon makes a move in this space, and Amazon has made several in 2019. On April 11, Amazon acquired Boulder, Colo.-based Canvas Technology for an unspecified amount. Canvas uses “spatial AI” to enable mobile robots to navigate safely around people in dynamic environments. It claimed that its combination of sensors and simultaneous localization and mapping (SLAM) software can enable AMRs to operate without relying on a prior map. The robots can continuously update a shared map, according to the company.


Amazon also developed new warehouse robots designed to accelerate automation in its fulfillment centers. Amazon said the new robots represent a major redesign of the Kiva Systems robots. Amazon warehouses already have 800 units of one of the new robots, Pegasus, up and running.





							
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5. ROS for Windows 10 official


Microsoft introduced last fall an experimental release of the Robot Operating System (ROS) for Windows 10. During its 2019 Build conference in Seattle, Microsoft announced ROS is now generally available on Windows 10 IoT Enterprise.


ROS is an open-source platform that provides robotics developers with a variety of libraries and tools to build robots. ROS for Windows 10 is an opportunity for Microsoft to expose its Azure cloud platform, and associated products, to ROS developers around the world.








6. iRobot introduces Terra t7 robot lawn mower


An iRobot robotic lawn mower was one of the worst-kept secrets in robotics. In January 2019, the iRobot Terra t7 robot lawn mower was finally unveiled. The Terra t7 robot lawn mower will be available for sale in Germany and as a beta program in the US in 2019.


Specs and pricing aren’t known at this point, but iRobot says ease of use is the main differentiator. Instead of burying and running boundary wires, users need to place wireless beacons around their yards and manually drive the Terra t7 robot lawn mower around to teach it the layout. The beacons need to remain in place throughout the mowing season. Terra uses the beacons to calculate its position in the yard. The robot will operate autonomously after the initial training run.








7. Big tech companies working on development tools


Add Facebook and Microsoft to the list of major technology companies working on robotics development tools. Facebook in late June open-sourced its PyRobot framework for robotics research and benchmarking. PyRobot, which Facebook developed with Carnegie Mellon University, is designed to allow AI researchers and students to get robots working in just a few hours without specialized knowledge of device drivers, controls, or planning.


On top of its ROS work, Microsoft is building an end-to-end toolchain that makes it easier for developers to create autonomous systems. The platform uses Microsoft AI, Azure tools and simulation technologies, such as Microsoft’s AirSim or industry simulators, that allow machines to learn in safe, realistic environments. The platform also uses what Microsoft is calling “machine teaching,” which relies on a developer’s or subject matter expert’s knowledge to break a large problem into smaller chunks.


In November 2018, Amazon Web Services released its RoboMaker cloud robotics platform to give developers a centralized environment to build, test, and deploy robots with the cloud. Google also has a cloud robotics platform that was announced last year.


8. Aria Insights shuts down


Drone maker Aria Insights abruptly shut down on March 21. Formerly known as CyPhy Works, the company was primarily known for its Persistent Aerial Reconnaissance and Communications (PARC) platform, a tethered drone that provided secure communication and continuous flight to customers.


CyPhy Works rebranded as Aria Insights in January 2019 to focus more on using artificial intelligence and machine learning to help analyze data collected by drones. But it was too little too late.


CyPhy Works was founded in 2008 by Helen Greiner, who also co-founded iRobot in 1990. Greiner left CyPhy Works in 2017 and in June 2018 was named an advisor to the US Army for robotics, autonomous systems and AI.


Robotics Investments for First 6 Months of 2019







	MonthInvestment Amount






	January$644M




	February$4.3B




	March$1.3B 




	April$6.5B




	May$1.5B




	June$1.4B




	Yearly Total$15.64B








9. Robotics investments


Investments into robotics companies have totaled more than $15.64 billion in the first half of 2019. Some of the leading markets investment-wise include healthcare robotics, logistics and manufacturing. But autonomous vehicles take the cake thus far. In June, for example, autonomous vehicles accounted for $717 million of the $1.4 billion that was invested into robotics companies.


Check out the table above for a month-by-month breakdown of robotics investments and follow our Investments Section for the latest news and analysis.


10. Johnson & Johnson acquired Auris Health


Johnson & Johnson (J&J) subsidiary Ethicon acquired Auris Health and its FDA-cleared Monarch platform for $3.4 billion. Auris is surgical robotics pioneer Dr. Fred Moll’s newest robotic surgical play. The acquisition is one of the 10 largest VC-backed, private M&A transactions of all-time and will be both the largest robotics and largest medtech private M&A deal in history. Kiva Systems previously held the title for largest robotics acquisition when it was purchased by Amazon for $775 million.


Auris’ robotic Monarch platform has FDA clearance for diagnostic and therapeutic bronchoscopic procedures. The system features a controller interface for navigating the integrated flexible robotic endoscope into the periphery of the lung and combines traditional endoscopic views with computer-assisted navigation based on 3D patient models. Auris said J&J’s global distribution will broaden access to the Monarch Platform.


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20 largest robotics investments during 1st half of 2019
	


	
	

		


robotics investments
An autonomous, all-electric Chevrolet Bolt from Cruise, which raised $1.15 billion in May 2019. | Credit: Cruise


Robotics companies raised more than $15.6 billion during the first half of 2019. According to the robotics investments tracked and verified by The Robot Report, more than $2.6 billion was raised on average per month. The year started slowly with $644 million raised in January, but there was at least $1.3 billion raised each month thereafter.


For The Robot Report‘s investment analysis, autonomous vehicles, including technologies that support autonomous driving, and drones are considered robots. On the other hand, 3D printers, CNC systems, and various types of “hard” automation are not.


Robotics Investments for First 6 Months of 2019







	MonthInvestment Amount






	January$644M




	February$4.3B




	March$1.3B 




	April$6.5B




	May$1.5B




	June$1.4B




	Yearly Total$15.64B







As you can see in the table below, autonomous vehicle investments made up a significant percentage of overall funding. Ten of the top 20 robotics investments tracked by The Robot Report belonged to companies producing autonomous vehicles or autonomous vehicle enabling technologies. Autonomous vehicle companies raised 55% ($4.6 billion) of the total $8.2 billion raised in the 20 investments. The top three autonomous vehicle investments belonged to Cruise ($1.15 billion), Uber ($1 billion) and Nuro ($940 million), which raised a combined $3.1 billion.


Healthcare robotics companies have also fared well in 2019. Intuitive Surgical raised $2 billion via a stock repurchase in February, while Think Surgical and Ekso Bionics raised $134 million and $100 million, respectively. HistoSonics raised $54 million in April for its medical robotics platform that destroy cancerous tumors without affecting surrounding tissue.


The Robot Report will have a detailed breakdown of investments by sector in a follow-up article.


To stay updated about the latest robotics investments and acquisitions, check out The Robot Report‘s Investment Section.


20 Largest Robotics Investments During 1st Half of 2019







	CompanyFunding (M$)Lead InvestorDateTechnology






	Intuitive Surgical 2000Stock Repurchase2/1/19Surgical Robots




	Cruise1150Honda Motor Corp.5/7/19Autonomous Vehicles




	Uber ATG1000SoftBank Vision Fund4/18/19Autonomous Vehicles




	Nuro.ai940SoftBank Vision Fund2/11/19Autonomous Vehicles




	Horizon Robotics600SK China2/27/19AI/IOT




	Aurora Innovation600Amazon2/7/19Autonomous Vehicles




	Weltmeister Motor450Baidu Inc.3/11/19Autonomous Vehicles




	Cloudminds300SoftBank Vision Fund3/26/19Service Robots




	Zipline190TPG5/17/19Drone Delivery




	Innoviz Technologies170China Merchants Capital3/26/19LiDAR




	Think Surgical1343/11/19Surgical Robots




	Beijing Auto AI Technology104Robert Bosch Venture Capital1/24/19AI




	Black Sesame Technologies100Legend Capital4/15/2019Machine Learning




	Ekso Bionics Holdings100Zhejiang Youchuang Venture Capital Investment Co.1/30/19Exoskeletons




	TUSimple95Sina Corp2/13/19Autonomous Vehicles




	Ouster60Runway Growth Capital3/25/19LiDAR




	NASN Automotive59.6Matrix Partners China1/30/19Autonomous Vehicles




	HistoSonics54Varian Medical4/8/19Medical Robots




	Ike52Bain Capital Ventures2/5/19Autonomous Vehicles




	Enflame43.4Redpoint China Ventures6/6/2019AI Chipmaker









Editors note: What defines robotics investments? The answer to this simple question is central in any attempt to quantify robotics investments with some degree of rigor. To make investment analyses consistent, repeatable, and valuable, it is critical to wring out as much subjectivity as possible during the evaluation process. This begins with a definition of terms and a description of assumptions.


Investors and investing

Investment should come from venture capital firms, corporate investment groups, angel investors, and other sources. Friends-and-family investments, government/non-governmental agency grants, and crowd-sourced funding are excluded.


Robotics and intelligent systems companies

Robotics companies must generate or expect to generate revenue from the production of robotics products (that sense, think, and act in the physical world), hardware or software subsystems and enabling technologies for robots, or services supporting robotics devices. For this analysis, autonomous vehicles (including technologies that support autonomous driving) and drones are considered robots, while 3D printers, CNC systems, and various types of “hard” automation are not.


Companies that are “robotic” in name only, or use the term “robot” to describe products and services that that do not enable or support devices acting in the physical world, are excluded. For example, this includes “software robots” and robotic process automation. Many firms have multiple locations in different countries. Company locations given in the analysis are based on the publicly listed headquarters in legal documents, press releases, etc.


Verification

Funding information is collected from a number of public and private sources. These include press releases from corporations and investment groups, corporate briefings, and association and industry publications. In addition, information comes from sessions at conferences and seminars, as well as during private interviews with industry representatives, investors, and others. Unverifiable investments are excluded.


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The Robot Report May 2019 issue on mobile robotics
	


	
	

		




We hope you enjoy the latest edition of The Robot Report, a special print section dedicated to mobile robotics. This appeared in the May 2019 issue of Design World, our sister publication and flagship publication at WTWH Media. Here is a breakdown of the mobile robotics topics covered inside:


Robotics Summit 2019 to take a closer look at mobile robots

Mobile robot engineers and users can learn from technology and industry leaders at the Robotics Summit & Expo, which runs June 5-6 in Boston.


What Amazon’s acquisition of Canvas Technology means

Amazon’s acquisition demonstrates the importance of safe navigation for developers and users of supply chain automation.


Augmenting SLAM with deep learning

SLAM is being gradually developed towards Spatial AI, the common sense spatial reasoning that will enable robots and other devices to operate in general ways in their environments.


Mobile robot trends from Automate/ProMat

At Automate/ProMat 2019 in Chicago, robotics developers checked out the latest products for manufacturing and logistics. Here are some robotics trends we saw at the show.


Expert roundtable: mobile robotics challenges and opportunities

A3’s Jeff Burnstein chats with leading autonomous mobile robot providers about market growth, technical challenges, and opportunities.


Integrating AI with fleet management software advances AMR collaboration

Data from new sensors, in combination with AI and machine learning, is making autonomous mobile robots or AMRs more flexible and safer around humans.


How 5G will impact mobile robots

Leading robotics companies share their opinions about how 5G will impact autonomous mobile robots.


If you are interested in contributing content to an upcoming special issue of The Robot Report, please reach out to me at scrowe@wtwhmedia.com or Eugene Demaitre at edemaitre@wtwhmedia.com. If you are interested in sponsorship opportunities of upcoming special issues, please reach out to Courtney Seel at cseel@wtwhmedia.com.
	


	





		

		
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Build better robots by listening to customer backlash
	


	
	

		
In the wake of the closure of Apple’s autonomous car division (Project Titan) this week, one questions if Steve Jobs’ axiom still holds true. “Some people say, ‘Give the customers what they want.’ But that’s not my approach. Our job is to figure out what they’re going to want before they do,” declared Jobs, who continued with an analogy: “I think Henry Ford once said, ‘If I’d asked customers what they wanted, they would have told me, ‘a faster horse!’” Titan joins a growing graveyard of autonomous innovations, which is filled with the tombstones of Baxter, Jibo, Kuri and many broken quadcopters. If anything holds true, not every founder is Steve Jobs or Henry Ford and listening to public backlash could be a bellwether for success.


Adam Jonas of Morgan Stanley announced on Jan. 9, 2019 from the Consumer Electronic Show (CES) floor, “It’s official. AVs are overhyped. Not that the safety, economic, and efficiency benefits of robotaxis aren’t valid and noble. They are. It’s the timing… the telemetry of adoption for L5 cars without safety drivers expected by many investors may be too aggressive by a decade… possibly decades.”


The timing sentiment is probably best echoed by the backlash by the inhabitants of Chandler, Arizona who have been protesting vocally, even resorting to violence, against Waymo’s self-driving trials on their streets. This rancor came to a head in August when a 69-year-old local pointed his pistol at the robocar (and its human safety driver).




In a profile of the Arizona beta trial, The New York Times interviewed some of the loudest advocates against Waymo in the Phoenix suburb. Erik and Elizabeth O’Polka expressed frustration with their elected leaders in turning their neighbors and their children into guinea pigs for artificial intelligence.


Elizabeth adamantly decried, “They didn’t ask us if we wanted to be part of their beta test.” Her husband strongly agreed: “They said they need real-world examples, but I don’t want to be their real-world mistake.” The couple has been warned several times by the Chandler police to stop attempting to run Waymo cars off the road. Elizabeth confessed to the Times, “that her husband ‘finds it entertaining to brake hard’ in front of the self-driving vans, and that she herself ‘may have forced them to pull over’ so she could yell at them to get out of their neighborhood.” The reporter revealed that the backlash tensions started to boil “when their 10-year-old son was nearly hit by one of the vehicles while he was playing in a nearby cul-de-sac.”


Rethink's Baxter robot was the subject of a user backlash because of design limitations.


The deliberate sabotaging by the O’Polkas could be indicative of the attitudes of millions of citizens who feel ignored by the speed of innovation. Deployments that run oblivious to this view, relying solely on the excitement of investors and insiders, ultimately face backlash when customers flock to competitors.


In the cobot world, the early battle between Rethink Robotics and Universal Robots (UR) is probably one of the most high-flying examples of tone-deaf invention by engineers. Rethink’s eventual demise was a classic case of form over function with a lot of hype sprinkled on top.


Rodney Brooks‘ collaborative robotics enterprise raised close to $150 million in its short decade-long existence. The startup rode the coattails of fame of its co-founder, who is often referred to as the godfather of robotics, before ever delivering a product.


Dedicated Rethink distributor, Dan O’Brien, recalled, “I’ve never seen a product get so much publicity. I fell in love with Rethink in 2010.” Its first product, Baxter, released in 2012 and promised to bring safety, productivity, and a little whimsy to the factory floor. The robot stood at around six feet tall with two bright colored red arms that were connected to an animated screen complete with friendly facial expressions.


At the same time, Rethink’s robots were not able to perform as advertised in industrial environments, leading to a backlash and slow adoption. The problem stemmed from Brooks’ insistence in licensing their actuation technology, “Series Elastic Actuators (SEAs),” from former employer MIT instead of embracing the leading actuator, Harmonic Drive, for its mobility. Users demanded greater exactness in their machines that competitors such as UR, a Harmonic customer, took the helm in delivering.


Universal Robots' cobot arms don't have the problems that led to a backlash against Rethink's robots
Universal Robots’ cobots perform better than those of the late Rethink Robotics.


The backlash to Baxter is best illustrated by the comments of Steve Leach, president of Numatic Engineering, an automation integrator. In 2010, Leach hoped that Rethink could be “the iPhone of the industrial automation world.”


However, “Baxter wasn’t accurate or smooth,” said Leach, who was dismayed after seeing the final product. “After customers watched the demo, they lost interest because Baxter was not able to meet their needs.”


“We signed on early, a month before Baxter was released, and thought the software and mechanics would be refined. But they were not,” sighed Leach. In the six years since Baxter’s disappointing launch Rethink did little to address the SEAs problem. Most of the 1,000 Baxters sold by Rethink were delivered to academia, not the commercial industry.


By contrast, Universal booked more 27,000 robots since its founding in 2005. Even Leach, who spent a year passionately trying to sell a single Baxter unit, switched to UR and sold his first one within a week. Leach elaborated, “From the ground up, UR’s firmware and hardware were specifically developed for industrial applications and met the expectations of those customers. That’s really where Rethink missed the mark.”


This garbage can robot seen at CES was designed to be cheap and avoid consumer backlash.


As machines permeate human streets, factories, offices, and homes, building a symbiotic relationship between intended operators and creators is even more critical. Too often, I meet entrepreneurs who demonstrate concepts with little input from potential buyers. This past January, the aisles of CES were littered with such items, but the one above was designed with a potential backlash in mind.


Simplehuman, the product development firm known for its elegantly designed housewares, unveiled a $200 aluminum robot trash can. This is part of a new line of Simplehuman’s own voice-activated products, potentially competing with Amazon Alexa. In the words of its founder, Frank Yang, “Sometimes, it’s just about pre-empting the users’ needs, and including features we think they would appreciate. If they don’t, we can always go back to the drawing board and tweak the product again.”


To understand the innovation ecosystem in the age of hackers join the next RobotLab series on “Cybersecurity & Machines” with John Frankel of ffVC and Guy Franklin of SOSA – February 12th in New York City, seating is limited so RSVP today!


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How Monteris Medical navigated a surgical robotics recall
	


	
	

		


Monteris Medical Neuroblate
Monteris Medical NeuroBlate robot-assisted brain surgery system.


Editor’s Note: This article was originally published by our sister website Medical Design & Outsourcing. 


Marty Emerson became CEO of Monteris Medical in July 2016. Within a month, the first report came in of a problem: The probe tip of the Plymouth, Minn.-based company’s NeuroBlate robot-assisted brain surgery device unintentionally heated up during the MRI-assisted procedure.


That discovery would eventually turn into a recall designated as Class I by U.S. Food and Drug Administration (FDA) – Emerson’s first in his roughly 30 years in medtech. Understanding and solving the problem would consume Emerson and dozens of Monteris employees over the next two years.


“Almost every emerging technology at some point or another in its maturation process has to go through one of those trials by fire, if you will, where you’re really getting into the core of your science and technology,” Emerson said.


Some regulatory experts said that although the company’s response to the problem wasn’t perfect, it appears to be out of the woods. In October 2018, Monteris won FDA clearance for a laser probe with fiberoptic-controlled cooling for NeuroBlate. The fiberoptic part replaced a metal thermocouple inside the laser probe, enabling Monteris to lift MR scan restrictions. All patient-contacting components are now non-metallic.


In late 2018, Monteris also announced that more than 2,000 patients have been treated with NeuroBlate since its release in 2013; the company also won reimbursement from Aetna and Anthem. Emerson is optimistic that the roughly $10 million a year company – which had seen annual revenue growth of 40% before 2018 – is set to grow again as it turns its focus to sales and marketing.


NeuroBlate uses a robot-guided laser to ablate brain tissue during MRI scans. Some brain surgeons find NeuroBlate a useful surgical option for certain epilepsy and brain cancer patients who don’t have many other alternatives, according to Emerson.


Monteris ticked off a lot of boxes for Emerson after he left the top spot at Galil Medical, the Arden Hills, Minn.–based interventional oncology cryoablation technology company he led until its 2016 acquisition by London-based BTG for up to $110 million.


A stint as a general manager for Boston Scientific in Singapore in the late 1990s, after joining Baxter in a finance role right out of college in 1985, was Emerson’s first foray into a management career that eventually led to the corner office at Minnetonka, Minn.-based American Medical Systems. (AMS’s male urology portfolio is now part of Boston Sci, and its women’s health portfolio is now Astora Women’s Health.)


Although his sales background and communication skills were what initially landed him at AMS, then-CEO Doug Kohrs told us, Emerson’s level-headed and numbers-oriented approach soon became apparent. Kohrs said he considered those unusual traits for a salesperson and eventually promoted Emerson to COO and groomed him for the top job.








“Marty took a very pragmatic approach to solving problems,” Kohrs recalled. “He wasn’t a sky-is-falling kind of guy. He just saw what was going on, and then he got the resources that he needed, and he fixed it.”


Frank Jaskulke, VP of intelligence at Minnesota’s Medical Alley Assn., described Emerson as among the most respected leaders in the state because of his work growing AMS, Galil and now Monteris.


He would need all of his skills after learning of the first unintended probe heating incident in August 2016.


“It became the No. 1 priority,” Emerson said. “We viewed this as an incredibly important initiative that had, at its core, a need to be intensely focused on the science and technology that supports our company.”


Company officials quickly determined the problem involved a coated metal thermocouple that helped measure temperature inside the probe. As Emerson explained it, the connector from the back of the probe to the system had sometimes moved too close to the bore of the MRI magnet, picking up energy that was transmitted down the probe and heating the tip.


The problem only occurred inside particular MRI systems running specific scan types, leading the Monteris team to test more than 20 permutations and combinations from companies including Philips, Siemens and GE.


In December 2016, as the company’s investigation progressed, another probe tip-heating case surfaced; two more incidents occurred shortly before Monteris alerted the FDA in September 2017. In one, a patient died of a brain bleed a few days after the procedure, although it wasn’t conclusive that the probe tip heating was responsible, according to the FDA.


Emerson said that Monteris came to FDA with a thorough understanding of the problem, data from testing the 20 MR equipment permutations, updated instructions for use designed to mitigate the issue and a product development plan to permanently resolve the problem.


Communication and transparency among the Monteris team, with the FDA and with physicians were front-of-mind for Emerson during this process, he told us, recalling a number of late nights when executives and regulatory experts jointly edited responses to the FDA. An accountant by training, he also tried to stay mindful of what he didn’t know.


“I’m not an FDA expert,” he explained. “I relied heavily on the scientists and the technologists and the engineers and the experts on my team to get us through this process.”


Did Monteris do enough?


Although Monteris appears to have done many things right and appears to have succeeded in eliminating the problem, according to regulatory experts, there are lessons to be learned for companies facing similar problems. Former FDA analyst Madris Tomes, now CEO of medtech safety software company Device Events, said she was especially impressed that out of the 342 adverse event reports she counted for the company since 2010, about half came from Monteris’ salespeople – a much better record than the industry as a whole.


“I’ve seen a lot of things handled much worse than this,” Tomes added.


Michael Drues, a Southern California-based regulatory consultant, questioned why more than a year elapsed between Monteris learning of the problem and alerting the FDA.


“Unfortunately, there is no regulation that requires this for a 510(k) yet – there is for PMAs – but a company does have an obligation, in my opinion, to let FDA know what is going on ASAP. This was a Class I recall, which has potential for serious injury and death.”


“There was never any suggestion from FDA that we didn’t move fast enough,” Emerson told us when asked about the time gap. “We were doing an immense amount of testing along the way.”


There were only two instances of probe tip overheating over the course of 12 months, he added. After Monteris issued updated instructions for use in early October 2017, the company received no reports of unintended heating for the year preceding FDA approval of its new technology, Emerson said.


As of press time, representatives for the FDA had not responded to a request for comment on the Monteris recall.


Monteris emphasizes thorough and complete adverse event reporting, Emerson said, adding that he strives to remember that the company puts its tools in physicians’ hands to help patients.


“The vast majority of the patients … are really well served by the technology that we’ve provided to those physicians,” Emerson said. “I can’t let an unfortunate outcome stop us.”


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Reinforcement learning shows promise for industrial robotics
	


	
	

		


Industrial robots deployed today across various industries are mostly doing repetitive tasks. The overall task performance hinges on the accuracy of their controllers to track predefined trajectories. The ability of robots to handle unconstructed complex environments is limited in today’s manufacturing.


Two examples are flexible picking of previously not encountered objects or the insertion of novel parts in assembly tasks. There are numerous examples of spectacular robot demonstrators exhibiting dexterity and advanced control, e.g. robot Fanta challenge, or robots playing ping pong. However, these applications are hard to program and maintain, usually they are the output of a PhD thesis, and they haven’t made the leap into manufacturing.


Endowing machines with a greater level of intelligence to autonomously acquire skills is desirable. The main challenge is to design adaptable, yet robust, control algorithms in the face of inherent difficulties in modeling all possible system behaviors and the necessity of behavior generalization.


Reinforcement learning (RL) methods hold promise for solving such challenges, because they enable agents to learn behaviors through interaction with their surrounding environments and ideally generalize to new unseen scenarios.


reinforcement learning
Figure 1: Reinforcement learning loop for robot control. (Credit: Siemens)


Reinforcement learning


RL is a principled framework that allows agents to learn behaviors through interactions with the environment. As opposed to traditional robot control methods, the core idea of RL is to provide robot controllers with a high-level specification of what to do instead of how to do it. Thereby, the agent interacts with the environment and collects observations and rewards.


The RL algorithm reinforces policies that yield high rewards, see Fig. 1. RL can be distinguished in value-function-based methods and policy search. In policy search, robots learn a direct mapping from states to actions. In value-function-based approaches, robots learn a value function, an intermediate structure that assesses the value of an explicit state, and derive actions from the value function.


Both policy search and value-function-based approaches can either be model-based or model-free. Model-free methods do not consider the dynamics of the world. Model-based methods incorporate a model of the world dynamics, which is learned from data as well.


Reinforcement learning for industrial applications


As we can see, robot control methods can be grouped along a continuum where on one end we find “rigid” feedback control laws, which are hand-engineered, incorporate domain knowledge and the control structure is not adapted by data. On the other end of the spectrum we have RL methods, which allow learning control policies purely from observed data. Both methods have advantages and disadvantages.


Traditional feedback control methods can solve various types of robot control problems very efficiently, such as trajectory tracking in free space, by capturing the structure with explicit models, such as rigid body equations of motion. However, many control problems in modern manufacturing deal with contacts and friction, which are difficult to capture with first-order physical modeling. And if higher-level reasoning is required (where to pick in bin picking problems, for example) current robot controllers lack flexibility. Applying feedback control design methodologies to these kinds of problems often results in brittle and inaccurate controllers, which have to be manually tuned for deployment.


RL, on the other hand, can, in principle, learn any control structure. However, for real-world robots, the continuous exploration space is large and, hence, large amounts of data and, therefore, long training times are required. Moreover, unlike conventional feedback control, convergence and stability statements are difficult to derive for RL methods.


Just to name two recently popularized use cases for both control methods: Boston Dynamics is known for deploying conventional feedback control laws (more precisely Funnel Control) for all its well-known demonstrations. Google, on the other hand, has shown that RL is capable to arrive at a robot controller for bin picking simply through trial and error. However, several months of training on a robot farm were required to achieve the required control performance.




After realizing that robot control methods comprise a continuum, where the underlying dimension is how much influence online data has on shaping the control algorithm, it seems that best control performance for flexible manufacturing has to combine both traditional control theory and data-driven RL. Traditional control can provide guarantees in safety and performance, while RL can bring flexibility and adaptability, if tuned correctly. In a way, RL removes the specificity needed at the engineering stage, where controls are designed. It targets to achieve the same performance than a carefully engineered feedback control algorithm, but without the need of tedious programming and rules.


We suggest decomposing robot control pipelines, which consist of perception, state estimation, control etc, into sub-problems that can be explicitly solved with conventional methods and sub-problems, which are approached with RL. The final control policies are then superpositions of both data-driven components and control policies from first-order models. Our approach combines the benefits of traditional control theory (e.g. data-efficiency) with the flexibility of RL. For example, position control is taken care of by a PID controller, and RL contributes the control part that deals with friction and contacts. We have conducted studies on different industrially relevant use cases, which amongst others include robots to perform real-world assembly tasks involving contacts and unstable components.


Figure 2 illustrates two assembly use cases, where conventional feedback control was combined with RL to solve complex assembly tasks in a flexible manner. Subfigures (a) and (b) show how a gear wheel is placed on a shaft. The use case is part of the Siemens Robot Learning Challenge. The robot required less than seven iterations to learn the required control policy. Subfigures (c) and (d) show a different use case for which the same control algorithm was used as for (a) and (b). Again, after less than seven iterations, the robot learned the control policy.


reinforcement learning
Figure 2: Insertion use cases solved with a combo of conventional control and reinforcement learning. (Credit: Siemens)


A challenge persists in this approach. Seven iterations may seem reasonable for lab setups, but they entail an inherent risk, as every iteration in a friction-rich environment has the danger to damage the part in contact with the gripper. Accurate sensors and adequate constrain management can alleviate the problem. Those are better handled in the pipelines that use traditional control, and can filter the output of the RL commands. Note that a certain amount of engineering is still needed to ensure that the robot is not in a lock position, unable to move because of the constrains. In these situations, calling a human for help may be the best course of action. In addition, in order to reduce the number of real world iterations, novel approaches in simulation to reality gap (sim2real) have been proven to accelerate the learning.


As a conclusion, we believe the current hype of reinforcement learning around robotic applications has a valid motivation; however, it is not the main ingredient to guarantee success. End-to-end learning approaches have shown poor performance in tasks that require precision. In an analogy that we like to make, if you want to make a chocolate cake, chocolate (reinforcement learning in this case) is not the main ingredient. You still need eggs, flour, etc. These “less-sexy” ingredients are in our case traditional control approaches. They are the base to build a successful flexible robotics application.


reinforcement learning
Figure 3: Siemens Robot Learning Challenge. (Credit: Siemens)


Robot Learning Challenge


We strongly believe that to accelerate robot learning research and its adaption in industry, we need a benchmark for the research community. We have seen that the ImageNet benchmark, which was introduced by Fei Fei Li in 2009, became the catalyst for image classification with deep learning. Machine performance for classification surpassed human capabilities in 2015. Benchmarks accelerate research because they facilitate reproducibility and allow comparison of research.


In the case of robot picking, this work goes in the right direction. In the case of robotic assembly, there is still need for globally accepted benchmarks. Therefore, we introduced the Siemens Robot Learning Challenge at the first Conference for Robot Learning in 2017. The challenge consists of a gear assembly task as seen in Fig. 2 (a) and (b) and Fig. 3. Details and CAD models for 3D printing can be obtained here.


Since the inception of our challenge, we have seen a variety of research work being published that is based on the Siemens Robot Learning Challenge – see examples here and here. We would like to encourage the community to try the challenge and help us refine it to cover as many cases as possible. Only with a common, easily reproducible benchmark can the robot learning community start building pipelines and tools that built on top of each other. If you have tried it, and want to contribute with your results, feel free to email the authors.


Aparicio


About the Authors


Juan Aparicio is the Head of Advanced Manufacturing Automation at Siemens Corporate Technology in Berkeley, CA. Aparicio has extensive experience managing complex projects, involving hardware and software; and bridging the technology gap between universities and businesses. His areas of interest include advanced manufacturing, advanced robotics, connected cars, Industry 4.0, and cyber-physical systems.


Aparicio is member of the Technical Advisory Committee for the Advanced Robotics in Manufacturing (ARM) Institute in the US and the Project Manager of the Open Process Automation Forum.


Solowjow


Dr. Eugen Solowjow is a Research Scientist specialized in robotics and machine intelligence at Siemens Corporate Technology. He has received his PhD from Hamburg University of Technology (TUHH), Germany. From 2012 to 2017 he was employed as a Research Associate at TUHH and as a visiting researcher at U.C. Berkeley.


Eugen was the technical lead in multiple government funded projects at TUHH in the field of robotics. He co-authored 20+ peer-reviewed publications (IROS, ICRA, RA-L, AuRo etc.) and has received multiple scholarships, fellowships, and academic awards.


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