And he has been in the industry for about eight years now. He had his education in California State University, and then he did a lot of automation work. He is now on his own with Spacewalk Automation.
Welcome again. I’m trying to understand how your early interest started, because this is something I usually ask — why did you get into robotics and automation?
So, I did not start out in robotics itself. Initially, after school, I actually wanted to study physics. That’s what I applied for in my degree. But after some time, physics kind of got boring because it involved a lot of theory and concepts. So I switched to computer engineering.
From that degree, I discovered a lot of cool stuff. While I was in college, I visited a lot of companies, including JPL, and both of those places were super hardcore into robotics. I liked seeing the actual machines and interacting with them. Getting to learn more about the field while being in that environment built a lot of interest and curiosity in me.
I also attended a robotics conference where I saw many different companies. That’s where I met the CEO of Boston Dynamics at that time. Feeling inspired, I joined a research program and worked there for almost a year on a new framework that Amazon was building, now called AWS RoboMaker. It’s a cloud platform where people develop software for robots, mostly for fleet deployment. It was still new, and we were testing it and building on top of that platform.
Because of that experience, I was able to get a job in automation as a firmware engineer for assembly line packaging machines in Southern California. I worked there for almost three years. During that time, we were also experimenting with industrial robots, including Universal Robots and other machines. The company was trying to develop applications using those robots.
At that moment, I realized these machines are everywhere. Almost every manufacturing plant I visited had them, and they’ve been around since the 1960s. Believe it or not, they are that old. Obviously, they were not as advanced back then as they are now, but every year they keep adding more features, more technology, more precision, and more strength.
I decided this field is going to be around for a long time. Surprisingly, in India, we don’t have any company of that scale with a global name. We know KUKA and ABB, but those are European companies. We don’t have anyone like that in India.
So I figured this was the perfect time to pack my bags, move to India, and start something.
At that time, there were many developments happening in India in terms of policies and manufacturing initiatives like “Make in India.” There were many startups in space, defense, and automobile sectors, and all of them need automation machines and robots for heavy lifting in factories.
I felt there was a lot of unmet demand for this. So I decided it was the right time to come back. I arrived around mid-2022 and started building. For the last three and a half years, I’ve been gathering the right people and working on research and development. It has been a struggle, but finally, we have achieved a strong version that we can now take to the market and see the reaction.
The primary goal of the company is to make manufacturing easier in India. It’s not that we lack talent, raw materials, or capital. We just lack the drive to create our own manufacturing advantages. Countries like Japan and Germany build their own systems and don’t import everything. That gives them leverage. If we control that layer of manufacturing technology, the scale at which we can grow is unimaginable.
We are not just a robotics company; we are an automation company. Our goal is to make manufacturing easier and scalable in India.
In terms of hardware, we haven’t done something entirely new because this technology has existed for decades. But what we have done is make it cheaper to manufacture. Right now, some components are still imported, but eventually, we want to build everything in India. That will significantly reduce cost and make it accessible to small and medium-sized companies.
Large companies can import machines from Europe or Japan without worrying about cost. Small companies cannot afford them at all. But there is a middle segment that has demand but no supply. We want to position ourselves to fulfill that demand.
Precision is critical in these machines. Humans cannot achieve that level of repeatability. Our prototype currently achieves strong repeatability, and in production, we expect to reach standards comparable to major global companies.
When we started building this, AI was not the big hype it is today. But once the AI boom happened, we reimagined what industrial robots could look like in an AI-driven world. Instead of building rigid, closed systems, we decided to make it open and developer-friendly.
We provide a Python-based SDK and an IDE that supports simulation, motion planning, and coding. Developers can test their applications in a digital twin before deploying them to the physical robot. Since we use Python, developers can integrate AI and machine learning libraries easily.
Most companies use proprietary languages. We chose to go with a common programming language to empower developers and AI researchers.
The digital twin allows developers to build and test applications without physically having the robot. They can simulate movements and ensure everything works before deploying it. This makes development easier and safer.
Currently, the SDK is not publicly available, but developers can contact us for access.
Regarding production, our prototype uses CNC-cut sheet metal. For production, we will move to casting for better vibration control, weight balance, and performance. We are also improving safety systems, internal wiring, electrical hardening, and certifications to meet industry standards.
Our pilot program is expected to start in Q2, where we will place robots in factories and gather feedback.
From a business perspective, machine sales are the obvious revenue stream. But we are also exploring financing models so customers don’t have to pay large upfront costs. Additionally, the software layer provides another opportunity. As developers build new skills for the robot, the machine becomes smarter over time. We are exploring monetization models around this ecosystem, potentially including an app-store-like model where developers can create and distribute new skills for the robot.
