This visual object intelligence platform makes industrial robots smarter
Imagine being able to order your phone, your car, or even your salad, all personalized to your liking, directly from a “one-size-fits-all” factory in your neighborhood.
This is exactly what the Bengaluru-based startup(Cybernetics Laboratory) hopes to make it possible one day. Founded by Gokul NA and Nikhil Ramaswamy in 2019the visual object intelligence startup says it automates the manual labor involved in manufacturing processes, opening up production possibilities.
This technological prowess in manufacturing, if achieved, will be akin to how computers have changed our lives by simplifying data processing.
“We are working on what is being called the holy grail of robotics – a visually enriched robotic arm that can grab, orient and place objects like humans do. This goal will be achieved through a combination of hardware innovation and extensive research in ML & Vision,” says Gokul.
Typically, industrial robots require customizations to meet production requirements.
CynLr (L:R) co-founders Nikhil Ramaswamy and Gokul NA
“Our vision is to simplify the engineering complexity and costly customizations that manufacturing faces today, which makes most automation unfeasible. We envision a universal factory, which does not need to be rebuilt every time the design of the parts it produces changes.
Universal factories is a futuristic concept where factories can transform into producing a wide variety of goods with a simple reprogramming or recycling of robots and machines.
“At CynLr, we envision universal factories becoming achievable with our visually intelligent robotic arms as a replacement for traditional part-specific robotic installations,” adds the co-founder.
According to the founders of the startup, universal factories will make today’s product-specific factories obsolete.
Today, due to all the custom infrastructure required to produce goods, a factory can only produce a small set of pre-envisioned variants of a specific product on an assembly line. For example, a Mahindra XUV 500 manufacturing line cannot immediately scale up to produce a Mahindra 700 because all automation and systems on the line are specifically designed for the components and dimensions that go into an XUV 500.
“In universal factories, assembly lines of visually intelligent robots can quickly transform to produce various types of components or products on demand, factories can adapt easily to changing designs and product variants, or perhaps even produce entirely different products. An assembly line of smartphones today, a line of healthcare PPE tomorrow, a line of rocket engine nozzles the day after tomorrow,” says Gokul.
The CynLr team
CynLr’s robotic arm is built as a standardized unit between different objects, different orientations and different tasks that can be reproduced quickly. This makes the assembly line more universal, where companies can reuse the line for different use cases, rather than having to change the whole setup even for a dimensional change in the dimension of the object handled. .
“We already have a working prototype where a robotic arm can ‘see’ and is able to use our property, built from the Visual Object Intelligence Engine to select, orient and place objects,” said the co. -founder.
Co-founders Gokul and Nikhil started their career at National instruments (NI) in 2011. Gokul says the time at NI gave them the opportunity to learn more about the limitations and impact of vision in industrial automation.
At NI, the duo observed that only three out of 10 problems attempted in Machine Vision are successfully solved. Machine vision involves both the technology and the methods used to help computers “see.”
“We soon realized that the existing vision paradigm was built on the assumption that identification is the only utility. It completely fails when it comes to manipulating or moving objects,” Gokul explains.
Gokul NA, co-founder and technical director, CynLr
“Upon further analysis, we realized that computer vision as a field has oversimplified the vastness of all tasks into a simple problem of identification, followed by indiscriminate manipulation of a machine or a robotic arm. And all the manufacturing plants that have used computer vision to solve their coordination problems have failed terribly,” explains the co-founder.
Gokul says that although attempts have been made to solve the problem of object manipulation, it has been done using the methods best suited to object identification. However, object manipulation and object identification were not treated as two separate issues.
So, Gokul and Nikhil developed an approach to solve this problem, but then realized that no hardware platform existed to universally apply his approach and solve all object manipulation problems. This is when they left NI in 2015 to start their consulting business Vyuti to test and prove this approach.
In 2015, the duo launched Vyuti to better understand the market by bringing their vision expertise to solve long-standing unresolved manufacturing issues. Working together on this problem for over a decade now, the duo claim to have solved over 30 real-world problems with a 100% success rate.
On the strength of this validation, in 2019 they raised their seed funding to work on the hardware and founded Cybernetics Laboratory (CynLr) to evolve their approach to a vision technology platform.
From a product perspective, the startup says it is in the pre-launch phase.
“We have pilots and POCs (proofs of concepts) that are paying in nature underway with Indian customers and global customers. In India, it is largely the automotive component manufacturers and automotive OEMs that we have engagements with,” says Gokul.
Among the pilot customers, the startup says it has a split of around 75-25 in terms of Indian and global (Germany, Italy, US) customers.
“However, when we begin to market on a large scale in 18 to 24 months, we expect this division to reverse,” he adds.
CynLr says its OEM partner is Ace Designs. He declined to release the names of his other clients, citing nondisclosure agreements.
The core team
The startup has a team of 20 members. An electronics engineer from BITS Pilani, Nikhil is the CEO of CynLr and handles management, sales and business operations. Gokul, who studied engineering at Amrita School of Engineering, is the CTO of CynLr and focuses on products, technology and brand.
Nikhil says that by the end of the year they would grow to over 50 full-time employees.
The YS design team
The road ahead
While there are many use cases for universal factories, the starting point could be automating manual tasks performed by humans, says the co-founder. According to a McKinsey report, this market alone is worth more than a trillion dollars in the United States alone.
Gokul is optimistic that the startup will unlock more value once it moves towards what it calls its universal factories vision.
“Think of this from the App Store lens. Once Google and Apple created App Stores to allow third-party developers to build apps for their operating system, tremendous value was created. The Numbers beyond the wildest imagination of companies or even business analysts,” he explains.
However, as a current milestone, the startup is focused on creating solutions for the robotics and automation markets for manufacturing and warehousing issues. According to its internal estimates, the market is worth $130 billion.
CynLr’s immediate goal is hiring.
“We are looking for people at all levels. Also, we are planning to expand into the US market this year,” says Nikhil.
The robotics startup plans to build capacity to meet the current customer pipeline and deliver 100 robots per year.
CynLr claims to have lifted total funding of $5.25 million until now. In 2019, the startup raised Rs 5.5 crore (around $720,000) in a seed funding round. The investment came from Speciale Invest, Arali Ventures, growX ventures, CIIE Initiatives, and investor Dr Vijay Kedia. In April 2022, it raised $4.5m in a pre-Series A funding round led by Speciale Invest and growX Ventures.
Speaking of competition, Gokul adds, “We are a multidisciplinary and diverse B2B (business-to-business) application company. Competitors are contextual. There are no direct competitors per se.