Reimagining Robotics and Manufacturing: Gokul NA on Vision Intelligence and Universal Micro Factories | Ep 1104

Summary

Podcast Summary: Reimagining Robotics and Manufacturing—Gokul NA on Vision Intelligence and Universal Micro Factories

The Digital Executive Podcast | Ep 1104—August 26, 2025
Host: Brian (Coruzant Technologies)
Guest: Gokul NA, Co-founder of Syndler

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Main Theme and Purpose

This episode explores the cutting-edge intersection of robotics, machine vision, and the future of manufacturing. Gokul NA shares his journey from National Instruments to founding Syndler, where his team is redefining “universal factories”—adaptive, intelligent manufacturing units drawing inspiration from neuroscience and human perception. The conversation centers on overcoming classic machine vision challenges, Syndler’s approach to robotics that mimic infant learning, and the promise of sustainable, decentralized production via microfactories.

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Key Discussion Points & Insights

How Early Experience Shaped Vision for Syndler

[01:20–03:45]

• Exposure to Limitations in Machine Vision: Gokul worked on vision, RF, and embedded systems at National Instruments, tackling complex, unsolved customer problems across the globe.
  • “For every 10 problems that we attempt, only three problems we could commercially succeed with all the platforms that we had.” —Gokul [01:53]
• Critical Industry Insight: Industry-wide, even major players (Cognex, National Instruments) could only solve ~30% of machine vision problems, especially in automation requiring object manipulation.
• Learning from Failure: Rather than focusing on what did work, Gokul asked: Why was the failure rate so high, especially in tasks needing robot manipulation and object handling?
• Human vs. Machine Limitations: Cameras provide just visual input, but for high manipulation tasks, humans use a blend of vision, touch, and context—an approach Gokul’s team sought to emulate.

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Solving “Unsolvable” Vision Challenges: Case Studies

[04:28–07:06]

• Consulting on Unsolved Problems: Gokul and his partner tackled over 30 persistent vision challenges after leaving National Instruments.
  • “We had to build [the camera] from scratch with the FPGA system.” —Gokul [05:56]
• Example Scenarios:
  • Bearing inspection: Counting and orienting pins obscured by grease—easy for a human, hard for machines.
  • Medical device assembly: Assembling intricate X-ray system components.
  • High-speed grain sorting: Identifying good/bad grains (or stones, glass) in free-fall at 5m/s within 56 microseconds—no commercial camera existed, so they built their own.
• Core Discovery: Across these use-cases, success required dynamic object manipulation—adapting to unknown variables rather than rigid pre-programming.
• Productization Thesis: “...there is a standard underlying principle which is solving all these problems, which means we could productize them.” —Gokul [06:42]
  • This formed the foundation for Syndler’s funding and technology stack.

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Emulating Human Infants: The Breakthrough in Object Intelligence

[07:46–10:44]

• Beyond Conventional Vision Systems:
  • Traditional vision depends on object recognition and color analysis, then feeds this to a manipulator—a process prone to failure on unknown or variable objects.
  • “We don’t treat motion as an independent sensing capability for a vision system.” —Gokul [07:59]
• Human-Inspired Robotics:
  • Infants can interact with and manipulate unknown objects by touch, motion detection, and adaptive learning—without needing to know what an object is beforehand.
  • Key insight: “We don’t have to know the object prior to acting on that object.” —Gokul [08:44]
• Technical Breakthroughs:
  • Syndler’s system leverages real-time motion and contour detection—mirroring the way human neural networks process visual and tactile data, specifically referencing the retina’s ganglion cells.
  • “Your eyes’ ability to highlight only the things that move together is what helps you stitch an object together.” —Gokul [09:29]
  • Force feedback is dynamically calculated, just as a human instinctively adjusts grip, optimizing handling for fragile, unknown, or complex objects.

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The Universal Microfactory and Sustainable, Decentralized Manufacturing

[11:32–14:40]

• Automation’s Rigidity Is Limiting:
  • “The rigidity that automation enforces...limits customers from reacting very quickly to customer needs.” —Gokul [11:34]
  • Traditional factories, designed for mass production, struggle to adapt to shifting or highly customized demands.
• Microfactory Concept:
  • Syndler is advancing “universal microfactories”—compact, decentralized, and highly adaptable manufacturing units.
  • Enables “franchise-style” manufacturing; smaller, more responsive, and able to continuously customize output.
    • “What McDonald’s has learned from Ford, Ford has failed to learn from McDonald’s...” —Gokul [13:11]
• Recycling & Resource Circularity:
  • Moving from mining raw materials to extracting value from waste (the “dustbin”)—made possible when robots handle the nuanced work of sorting and reclaiming materials.
  • “When you are able to build a system which is capable of applying that nuanced skill as much as a human...then you have a free effort available to extract material from your wastages.” —Gokul [12:45]
• Economic Efficiency:
  • Flexible, universal automation lowers both startup costs and waste; production lines can rapidly switch between different products without costly retooling or redesign.
  • “If I could produce all cars in the same line...you don’t have to look for hyperproduction… or create artificial market demand for it.” —Gokul [13:37]
• Core Vision:
  • Universal microfactories could change the scale and accessibility of manufacturing, making production more local, responsive, and sustainable.

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Notable Quotes & Memorable Moments

• On Failure as a Learning Tool:
  • “Where are we failing the most instead of seeing where are we succeeding the most.” —Gokul [02:28]
• On Human-Like Robotics:
  • “A human being is capable of isolating an object out of [clutter], even though he doesn’t know what he’s actually picking.” —Gokul [08:18]
• On Changing the Manufacturing Paradigm:
  • “The ability to franchise your manufacturing is missing.” —Gokul [13:05]
• On Materials and Recycling:
  • “Why do we always look to mine the earth...rather than trying to mine that out of the dustbin?” —Gokul [12:27]

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Timestamps for Important Segments

• 00:00–01:15: Intro, background on Gokul NA and Syndler’s vision.
• 01:20–03:45: Challenges in machine vision; why previous approaches failed.
• 04:28–07:06: Consulting on “unsolvable” problems; real-world examples and key insights.
• 07:46–10:44: Breakthroughs in mimicking human infant learning for robotics; motion and force feedback intelligence.
• 11:32–14:40: Decentralized, sustainable manufacturing and universal microfactories.
• 14:40–15:38: Closing thoughts, highlighting the transformative potential of universal microfactories.

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Conclusion

This fast-paced episode distills how a neuroscience-inspired vision system and adaptable microfactory platforms could transform manufacturing—making automation more human-like, versatile, and sustainable. Gokul NA’s approach blurs the line between human learning and machine intelligence, aiming to unlock local, decentralized factories that flexibly and efficiently serve the emerging needs of a dynamic market.

Transcript

[A]

Foreign.

[B]

Welcome to Coruscant Technologies, home of the Digital Executive podcast. Welcome to the Digital Executive. Today's guest is Gokul Na. Before founding Syndler in 2019, Gokul began his career at National Instruments and worked as a software architect and specialist in vision, RF and embedded systems. He was responsible for designing algorithms, serving customers across Europe, Taiwan and India. After a consulting stint and successfully solving 30 plus customer problems that were previously unsolved vision conundrums, Gokul and Nikhil started Syndler in 2019 to productize their vision approach at Sendler. Gokul, along with Nikhil is building the organization to address technology and industry gaps in achieving the idea of universal factories. This requires foundationally rebuilding vision from the understandings of neuroscience to custom building imaging systems involving 400 unique parts and 200 partners from across the globe and reinventing the industry framework to handle these operations, unique supply chain, internal tools, processes and talent.

[C]

Well, good afternoon Gokul.

[B]

Welcome to the show.

[C]

Thank you so much for hosting me. Brian. Pleasure to be here.

[B]

Absolutely, my friend. I appreciate it. You're hailing out of Bangalore, India. Currently I'm in Kansas City. So I appreciate you staying up late to do a podcast with me today. So Gokul, I'm going to jump into your first question. You began your career at National Instruments as a SW architect specializing in vision, RF and embedded systems, designing algorithms for customers across Europe, Taiwan and India. How did that experience shape your approach when founding Sandler in 2019?

[C]

So it exposed a lot to the gaps that we had in the machine vision spectrum and especially when it comes to automating tasks, using vision as a feedback or a sensory system for you to kind of handle. One of the primary problems that we used to notice back in the time is close to if you have like 100 problems, only you know, 30% of those problems will 30 problems we will be able to solve. For every 10 problems that we attempt, only three problems we could commercially succeed with all the platforms that we had. And that seemed to be a problem that we had across machine vision as a domain. The stalwarts like Cognex, National Instruments, Keyens, all of us faced some of these issues. So we had certain soul searching at the time to see why is that vision success rate compared to the other product lines that we had within our companies was struggling so much to solve things. So one of our each of them took different approaches and they kind of pivoted their businesses to different segments. And one of the factors that I was analyzing is where are we Failing the most instead of seeing where are we succeeding the most. Often it seemed to be that when you wanted to use vision systems or visual feedbacks to guide machines to do manipulation on objects, pick objects or handle objects or move objects is where we were failing the most. Even if you want to do an inspection right when it's very static printed text and things that doesn't need your head to move or need you to observe from a different point of view is where mostly successes were happening. So this made us to ponder more on what are we missing in terms of understanding the vision's need for manipulation and what's missing in those cases. And that shaped later for us in all the tech stack that we build. And also to make the robot a lot more self reliant. And also notice how camera is very limited. It gives you only sight. Vision is more about the involvement of touch and then grasping along with the visual sensing and the, you know, camera based sensing. So that became the basis for cybernetics. And then what we do are cybernetics labs here.

[B]

And thank you, I appreciate that. And I didn't know that the success rate for, you know, those problems trying to be solved only three out of ten problems, gosh, that's, that's certainly an uphill battle there obviously. But I liked how you said you dove into what was not working, not focusing on what was working and using this type of technology, using vision to make it successful. And I just love your tenacity there and diving in. So I appreciate that. And Gokul, before Syndler, you and your co founder consulted on over 30 vision challenges that were considered unsolvable. What's a standout success story from that period and how did you reinforce your decision to productize your vision approach through similar.

[C]

I think every problem kind of stood out so because most of them were remaining unsolved for a longer period of time because they were picking them from a very traditional machine vision approach point of view. I'm not talking about machine learning alone. I'm talking about the whole physics of how light works, how sensors work, how does a human brain go about analyzing it or how they are able to pick the aspects of objects and then analyze them. Right. So how your brain goes about kind of understanding all these aspects of the object.

[A]

Right.

[C]

So the problems like. So we had situations where you had a simple, very simple problem like a bearing, it's a roller bearing. On top of this you have the needles and pins and you need to be able to understand in spite of the grease that is going to Be there, how many pins are there, whether they are properly oriented, whether the surfaces are good without having to remove those greases. A human being seems to be much more adept in doing that from there all the way to an X ray college set that you have to kind of assemble for a GE healthcare, right? It's a six axis systems and each of those are like very complicated mechanism that goes within them close to around like maybe around 15 to 20 parts that have to be put together, assembled and then put together. And all the way till even one particular problem of grain sorting grains and rice grains, where you need to do more than a ton of of grains that flow in free fall and gravity, naked eye, you can't even see them. They move at like 5 meters per second of speed. And yet you should be able to find out where those grains are. What is the background, is it a glass piece, is it a stone, is it a good grain or a bad grain? And all that within 56 microseconds. So on the other hand, you didn't have a camera system off the shelf that can actually understand from that kind of details, right? And that speed. So we have to build that from scratch, the whole camera from scratch with the FPGA system. So we did a wide variety of things underlying all this, there was an aspect of dynamic visual manipulation in every case where you are guiding motion, you are handling variations in the objects which you have not been pre programmed for, prepared for. And all those aspects is what we borrowed back later. So we took our thesis that we learned from National Instruments, went out of NI in 2015, took all these 30 problems, all wide varieties of manipulation issues where you're handling real world objects in different paces, different speeds, different formats for different purposes. But does my thesis work as a consistent one across all of these problems? So that became proof for us later to say that, okay, there is a standard underlying principle which is solving all these problems, which means we could productize them. And that's the thesis with which in 2019, August, we raised a fund to productize what we have learned from our previous 30 systems.

[B]

Amazing, I appreciate that. You know, I like your unconventional approach to tackle these unsolvable challenge. And you really parallel it with it. How does a human being, how does a human brain perform these tasks? And the fact that you have to build something from the ground up, you built that camera right as there was nothing in the market that you were able to utilize. So building it from the ground up, understanding how that works and again, just amazing what you're doing, gokul your cyber robot system, powered by object intelligence, can handle unfamiliar objects in real time, much like a human infant. Where did this idea come from and what are the key breakthroughs that made this possible?

[C]

This is again, I mean the learning for this again came through. What are the other two questions that you asked before? Where our experiences were in the past in analyzing what is that we are missing in the vision side when it comes to manipulation and manipulation oriented tasks for it to guide is we don't treat motion as an independent sensing capability for a vision system. We always think it's a video processing and we do an optical flow. We track them from that point of view. What we don't understand is, or more than that, the way we approach in computer vision is we are hyper reliant on color for everything. Color pattern is the way we go about it. And we think that identifying an object and then later just finding its location and then feeding to a robotic arm system is enough for it to go and operate onto them. And if they fail, we either think the identification process has failed or the manipulation process has failed. And then we go and overemphasize on improving the designs of Gripper or we go and overtrain our machine learning models for identification. On the other hand, human vision like a baby, right? Which doesn't need any of the prior understanding of the objects, right? Edit is able to instantaneously go act on them. Like if I go give this airport, it doesn't know that it's an airport, it picks it in its mouth, throws it picks it with his hand, puts it in the mouth and throws it out, right? That's such an inherent ability. So any clutter you put them into, a human being is capable of isolating an object out of it, even though he doesn't know what he's actually picking. So this means we don't have to know the object prior to acting on that object. And most of the failures here is my ability to inability to identify that orientation of the object. So which means I should be orientation agnostic to be able to go pick them. So but if I don't know an object, how do I go and pick it up? So that's the analysis that went into our process. And what seems to be happening is we are very easily able to see motion in our fourth layer of our neural network called as on of ganglions. We use that ability to see the motion to connect all the disconnected contours. Because colors are always a cacophony of colors that you are filled in with now stitching one color to another color and creating an object out of it. This happens by the fact that I'm able to recognize one contour. I go and touch that contour. When I touch and then move that contour, somehow every other color contours that were there are able to associate with them and move along with it. Your eyes ability to kind of highlight only the things that move together is what helps you to stitch an object together. So that's the ability that we bring in into the camera and object intelligence also. And the second factor is when you're anticipating to touch them, how do I know how much force to apply? How hard is it? Is it a soft material? And how can you heuristically go and correct your force applications onto it? What does the color mean in terms of force for me in real world? So these two aspects is what we focused hyper a lot on and we mirrored these behaviors from the human brain and we replicated some of these initial layers of our brain into it. Right. So that's what we call us also as an object, visual object intelligence. And this is the basis by which the system, even if it's a mirror finished object whose color patterns are completely unknown, transparent objects which just takes the background as its color feedback, it can still go and act on them, pick on them.

[B]

Wow. A lot has gone into this. Obviously you've not wasted a minute of your day in doing the research. And I just love this in this learning process. You, you know, when you started you asked what is missing in this vision technology. And of course you're able to use motion, color patterns, contour, et cetera. There's a lot of things in there. You know, you talked about porous applications, you know, what type of pressure would be needed to. But at the end of the day you're mirroring this from a human brain. And I think that's amazing how we're creating robotics at this level. So I appreciate that. Gokul, the last question of the day at the AI for Good Summit 2025, you presented how decentralized microfactories can enable sustainable and personalized production. How will Sendler's approach reduce waste and make advanced manufacturing more accessible?

[C]

So one of the primary factors that we have today is in the industry is the rigidity that automation enforces to the infrastructure, which also limits the customers to be able to react very quickly to customer needs. When the dear consumer and customer needs. When the product designs have to change, they become a slave of manufacturability on one side, on the Other hand, the other vision of we always keep mining from like for example, repair. Converting your repair centers into factory is not possible today. And we are thinking of gigafactories rather than thinking of renos and micro factories on one side. So if I'm able to move all my products to be customizable, a lot of wastages actually go off from there.

[A]

Right?

[C]

And basically a concept of customized consumables or customized products. Why is it so hard today? Because you need a effort that transforms itself from one model to another model without any additional infrastructure needs.

[A]

Right.

[C]

One second, why do we always look to mine the earth for your new materials rather than trying to mine that out of the dustbin? That's because effort to extract the material out of your wastages are higher than the effort to extract it out of the earth. And as in terms of volume and the cost of the effort more than the effort that is being higher, the cost of the effort is being the bigger problem. When you are able to build a system which is capable of applying that nuanced skill as much as a human being is able to, and it's a one generic universal form factor that's able to do for wide varieties of items that can actually handle, then you have a free effort available extract material from your from your wastages. So that allows you to transform this whole concept of looking to raw materials from earth to looking to raw materials from the dustbin. This also makes another transformation for the customer. What McDonald's has learned from Ford, Ford has failed to learn from McDonald's is a typical statement I use to open these concepts. But the ability to franchise your manufacturing is missing. Second, because of which, if you're going into a new market and you want to set up a car manufacturing plant, you're spending anywhere between $300 million to $1 billion to set up a line. I'm just throwing a number like approximately. If you're not able to sell a 30,000 units out of it produce every month, then you are not profitable out of that line. On the other hand, market might say that I need four models and they are interested in and you need four models to reach to the 30,000 cars. But for every model you are now forced to build new lines. What if I could produce all cars in the same line, all platforms in the same line as I run under SUV in the same line. So you don't have to look for hyper production. Maximizing a production create a sales model and artificially create market demand for it.

[A]

Right?

[C]

So that also saves on a lot of material waste and also in the amount of investment that you're put into the. Into the factory today.

[A]

Right.

[C]

So if what is the bottleneck there? An infrastructure that is morphing itself to be able to act between two entirely different objects or products. So if I have a platform under a robot system that's able to switch over between two different tasks back to back and not have the issue that human beings typically have with context switching and produce the consistency of a machine as it is, then we have a phenomenal transformation that's coming over what we call as the universal microfactories.

[B]

That's amazing. I never really heard that concept. But you did delve into quite a bit here and just to highlight a couple of things. We know that the market, the customer needs changes in a heartbeat in. And how do we adapt so quickly? And manufacturing's not easily able to adapt, as you know and you explained there. But I liked what you highlighted. Ability to franchise. Your manufacturing is missing and you're looking to solve these things. You dove into a little bit of that. Extracting raw materials versus material waste, recyclable other alternatives to manufacturing. And the last thing, of course, having robotics being able to multitask, which humans can't do very well. But if we can do that, then again, as you said, machines can really do well.

[A]

Yeah.

[B]

Yep, yep. So I appreciate that. I really do. And go, cool. It was such a pleasure having you on today and I look forward to speaking with you real soon.

[C]

Thank you so much, Brian. Thanks for giving a platform for me to express these thoughts and ideas that we have created at Signler here. Much appreciate the opportunity.

[B]

Bye for now.