Episode Transcript
[00:00:03] Speaker A: Welcome to AINA Insights where prominent leaders and influencers shaping the industrial and industrial technology sector discuss topics that are critical for executives, boards and investors. INA Insights is brought to you by INA AI, a firm focused on working with industrial companies to make them unrivaled segment of ONE leaders. To learn more about INA AI, please visit our website at www.ina.AI.
[00:00:40] Speaker B: Good morning. Welcome to another episode of AINA Insights podcast. Today we are thrilled to welcome Rishabh Agarwal, CEO and co founder of Peer Robotics. Rishabh has spent his career reimagining industrial operations through the development of collaborative robots that simplify complex processes and empower shop floor workers.
Peer Robotics is transforming how manufacturers handle material movement, offering intuitive, easy to deploy robotic solutions that don't require specialized programming skills. Rishabh's journey combines a strong academic foundation earning degrees from IIT Delhi and the University of Maryland with hands on experience at industry leaders like Kuka Robotics and Siemens. Rishabh, welcome to our podcast show.
[00:01:35] Speaker C: Thank you Prates, really appreciate it. Thank you for having me and looking forward to our discussions.
[00:01:41] Speaker B: Likewise, thanks for making the time.
So what inspired you to launch Pure Robotics?
[00:01:47] Speaker C: So that's actually an interesting story. I come from a manufacturing family, so my entire family are different types of manufacturers, let's just say that.
And I always saw the big challenge that small and medium scale manufacturing industries face when it comes to automation. And over the years working with other companies like super was like one of my first internship time periods, like long time back and then after Siemens as well quickly realized that the solutions we have are really complicated and complex and expensive.
And that's where it really came to us that if we can build something simple and intelligent, we might be able to go in and help even small and medium scale manufacturers that don't have anything. So it wasn't that simple. That's kind of like the idea why we started Peer Robotics. That's the idea that is still guiding us and our principles. So that's it.
[00:02:40] Speaker B: Got it. Really interesting. So how is Pure Robotics using technology to reshape material movements and collaboration on shop floor with these manufacturing manufacturing customers clients that you are working with?
[00:02:55] Speaker C: I think like our biggest idea there was that what is the most intuitive way that people on the shop floor can teach robots and we had seen with other robots like the manipulators and all, but how easy it could be for the people on the floor to just show them by imitation learning. And our idea is that can we use the same level of technology for mobile robots and allowing These mobile robots to act almost like a trolley, so person can grab it, move a trolley. But then in this process, this trolley is kind of learning about the environment and how to do that fast.
So that's kind of like the core technology that we use, but we built up on top of it using our vision systems and layered intelligence, which essentially allows our robots to understand the environment in a bit more detailed pattern. Like what is a dynamic object, what is a static object, what is a trolley, what is a pallet? So users don't have to define every single thing or hard code the programming of the robots.
[00:03:56] Speaker B: Got it? Now that's helpful. Could you give us a real world example so our users can visualize what you are referring to? How are your robots helping the streamlined operations on the floor?
[00:04:11] Speaker C: Yeah, certainly. I think one of the example I would take is for one of the electronics industry that we work with, they're one of the major phone assemblers. And how we work with them is that they have these storage lines where a lot of raw components like resistors or different components that have to go into their PCB SME lines needs to be fed. Now for them, they constantly change their lives because currently they might be using a Model X and tomorrow they might be making a Model Y. So they need to adapt these systems. So what our technology does is that it allows these manufacturers to literally just grab the robot, walk it like a trolley. But then in this process, the robot learns about the space and they can teach it that this is the pickup point, this is the drop point, and whenever you get this API call or a trigger from a user, you go and do the sequence and the next time onwards, robot does that.
So it becomes very simple for the operator on the floor because they can go and program the robot and if something changes tomorrow, they can change their workflow by just showing the robot again that exact task.
Got it.
[00:05:22] Speaker B: So I'm going to switch topics a bit. You've raised a significant amount of funding, so that's a great validation of the peer robotics concept.
So as with this raise, what growth strategies have worked and how do you plan to deploy this to keep innovating?
[00:05:41] Speaker C: That's a good question. And I think there are a lot of things that we did right, did wrong, and how we are building up on it.
The core fundamentals, why our raise has helped us is essentially allowing us to build a stronger R and D and development muscle. Now, what I mean by that is even at a size of 25 people right now and globally, deploying like US, Europe, India, we have active deployments. We still have only one person that I can categorize as a salesperson. Rest of the entire team is R and D.
What that means is we focus on hiring top of the top kind of engineers. And we don't have a separate deployment team or integration team, we just have an R and D team. So people who are also developing these features are going at the customer site, working with them to get the first hand experience of, okay, what are the right features we have to come and develop in the future? What are the things we are doing right? What are the things we are doing wrong? And I think that's where the capital has been really helpful for us because we want to build a team that is actually just categorized into two way customer support and development. Right. And we want to have our same engineering team do both of it. So this allows us to hire best people, but also give them time and space to go out and develop with customers.
[00:07:01] Speaker B: Got it. Now super helpful to understand that journey that you are on. So related to that, as we or as you see most of your customers Moving towards Industry 4 or more automation, what role do you see the collaborative robots playing in addressing labor shortages, efficiencies and similar challenges that manufacturers of all shapes and sizes are facing?
[00:07:29] Speaker C: Yeah, I think one thing that these collaborative robots really could open up is how many application a single type of platform can do. Which is the true potential of robotics. Right. Like we always talk about it, what is the potential of robotics? And that's the true potential that what all areas the robots can add value because there is no single person who is doing only one thing on a shop floor. It's actually very hard to find a person who's doing only one thing. Usually they are associated with multiple tasks or they are helping out on other areas as well which are adjacent to their main operation. And I think that's the potential of collaborative robots, that their simplicity and their ability to work with humans, learn from humans can really open up applications which might not be feasible right now. Now what I mean by that is let's say we have robots that can do trolley movement, pallet movements or bend movement.
We are doing that, we are automating that. But then on top of it, these robots are also able to collect data that, okay, how much consumption is happening on line one, how many raw material has been consumed over this period of task, and then come back to the operations manager and provide the data that hey, this line seems to be running slow, we should add more people on that or we should kind of feed or slow down our feeding of raw material into this line.
And that's the next era that we see which is like layered intelligence on top of these existing collaborative robots that can enable more and more use cases and faster ROI for industries of different scales.
Got it.
[00:09:09] Speaker B: Super helpful. So I'm going to switch gears a bit and talk about Rishabh and Rishabh's journey. So a couple of questions on that.
So your experience at Kuka Siemens and your educational background at IIT Delhi University of Maryland. How did all these come together and play a role as you put together pure robotics?
[00:09:35] Speaker C: Yeah, it's a very exciting. And if I remember kind of correctly from those days, I started my journey as a mechanical engineer. Like, that was something that I was passionate about because I saw my dad and my grandfather kind of tinkering along on these industrial machines as long as I remember. So mechanical was always like a core part of me. But when I came to IIT Delhi, I had a very, I would say, influential advisor and mentor even till this date. Like, he's a very core advisor for me, Professor S.K. sahaf, who gave me, or like, who directed me towards some of this new industry, which is like robotics. Right. Like, for me, that was like the first times getting in touch with robotics or starting to work in the field of robotics. But that quickly took me towards this area about, okay, there is a potential of new mechanical systems which are more intelligent, which are more dynamic, and that's robotics. So started working on that area, slowly came towards the biggest problem, which is like, how do these robots communicate with humans? Because they always seem to have a mind of their own and they would never work when we want them to work. So that was one of the things that became my focus early on as I started working with robotics, which is around human robotic interaction. I took the same thing and started working in Maryland under Professor Sarah Burgbrighter and her research around haptic sensors for allowing these robots to understand a lot more about the environment than the vision or other platforms can do. So we worked on several tactile sensors, how you can learn or understand what kind of object these robots are picking up or even if a human is trying to interact with the robot. So that was again along the lines of how we can make these robots understand a little bit more about the environment or humans. And along these ways, I was always working with industries in parallel. So Siemens was trying to develop something similar, worked in that group for some time to figure out how we can make these collaborative or soft actuators and sensors so that we can get a lot more knowledge about the environment. Plus was in a collaborative fashion.
And this kind of like really put me towards that. Okay, there is a way that these robots could be simpler and that is to bring human in the loop. Learning from humans, utilizing what humans already know and building up on the repeatability and efficiency that the bots really bring to the shop floors. And I think with the kind of advancement around a lot of visual language models and learning about the environment and having a higher context understanding, I think now we are going into an era where okay, what are the other intelligence things that these robots with their data can also provide back to the humans?
So it's an interesting journey, but I think from early on started from mechanical engineering and quickly went more towards human robot interaction and how humans can learn from robots.
[00:12:35] Speaker B: Definitely sounds like an interesting journey. I would like to pull the thread on the family dynamics. Yeah. Again looking at growing up in that manufacturing family environment where both your parents, your father, grandfather, working on mechanical problems. How did that exposure shape your vision and approach as you embarked on this entrepreneurial journey in the industrial space?
[00:13:07] Speaker C: Yeah, I think the manufacturing families always have this dynamic. So at least from my limited point of view, where they are exposed to a lot of challenges and difficulties that manufacturers face early on. And that's actually very valid concern. Right. Like if you look into the industrial base, most of the industries rely on small and medium scale, which my family belongs from, as well as a category like they are small scale manufacturers building something that is locally required and that makes financial sense to produce locally and grow from there.
But if you look into the challenges, their challenges are essentially the same as large enterprises challenges, but even more or even more elevated. Because if there is a person who is looking into becoming a welder, they would rather go for a large enterprises enterprise job than a small company who might be offering the same job. Even if they offer the same amount of money. It's just the growth could not be there that a large enterprise could offer. Right.
So hiring even in countries like India or those limited talent full was very difficult. So if you look back at it, actually the small and medium scale enterprises are the ones who need the automation the most. Because they are located somewhere isolated, not in middle of some big cities or hubs of these manufacturing.
And they are trying to build something which requires specialized talent. And I think if we pull the thread a little bit more, and that's what again we saw, like I saw personally over the years, is that small manufacturers constantly change their operations to meet the dynamic requirements. Right. Like they are guided more by the large enterprises than the market dynamics. So what that means is where Toyota or Ford or any of the major companies can have one production line running for years and years, small manufacturers might have to change the component they are making at much faster frequency because they are making for different companies and they change their lines and they are on their timelines. So the need for flexibility and adaptability is again multifold in small manufacturer than a large enterprise.
But that's what we saw that whenever we look into automation they were only limited to large enterprises. And almost small manufacturer like might even laugh at the aspect of okay, bringing automation on the shop floor. Because it's just like from their point of view, it's too complex, they are too small, they can't invest time and money. Right.
And that's like the things that I was exposed early on and I truly believe we can change that.
So I'm glad that the family gave me that exposure because otherwise that would have been a problem in the hindsight and would have never been able to pull that thread.
[00:15:59] Speaker B: Totally makes sense. Great experiences that shaped the outcomes that you're working on. So that kind of helps us pivot back to the to talk about industrial robotics and what does the future hold? So for these small medium manufacturers, when you think about designing your robots, how do you kind of go about designing them for these, I would call them semi technical to non technical users and make it user friendly and intuitive so they can start using it and getting the benefit out of it.
[00:16:38] Speaker C: That's a very good question. And I think I would take it down into three buckets. But we have to ensure that all these three things are catered to. One is that we cannot come in and ask them to change their assets or infrastructure. And both are very important. Like we can't come in and say that hey, use this type of trolleys or this type of bins or this type of pallets so that our robots can automate it. That's like saying the engine manufacturer or some other specific component one that you should make this component so that our robot can handle it. Not a chance at all. So that's first aspect that we have to build robots that can automate existing assets in the existing infrastructure without changing anything. The second aspect is the intelligence that how the robot understands the context around the environment with minimal feedback from the user.
So how can we retrain the robot more and more on some of the operational part that users might not need to program it on the robot because Again, it's going to be dynamic and people would keep changing it. And if they have to do it again and again for the robots, like program it, the cost would bounce off. And the final thing is simplicity in general. Like, you know, how can we make the interfaces, the dashboard, the communication to the robot in such a way that it's very intuitive for them? And we work on all three parameters. We are a vertically integrated company, so we develop our own hardware. And it started from our hardware innovation. We have a product that allows our robots to handle existing assets. We don't change anything on the trolleys or pallets. And actually the same robot with a different end effector can handle both pallets and trolleys. We haven't gone out with the market yet with this particular application, but we'll be releasing that soon as well, and we'll love to talk or show more about that robot. The other area is the intelligence, and that's where we are heavily focused on vision systems. We train our robot on RGB data, not just on the LIDAR data. LIDAR is critical for safety. We have systems on our robot that ensure that safety is taken care of. But again, vision really allows us to unlock a lot of operational intelligence that we're missing.
And finally, a lot of our user interfaces, the dashboards that we have and the APIs that we use for communication is so simple that it's literally like using an iPad app. So you can use your existing iPad and the user can literally trigger it like a calling station, so they don't have to work too complicated way. Like they can just press a button and the robot will take care of all the movements, making sure the mission is executed and given back to the user that, yep, I've completed that task and this has been done. And this will. So. So it's like a mix of these three operations. And I think equal importance is there. We can't kind of like do one really good and the other two in a slightly better pattern. I think that's what makes robotics challenging. It is actually running two or three companies in a single umbrella, but I think all three is critical.
[00:19:44] Speaker B: So there's. Thank you for that. That was super helpful. So I see there's a lot of technology that you' in, but you're simplifying it.
So when I think about pure robotics in terms of R and D and the investments you're making, where. What is the thrust of the investment that is focused on enhancing the.
And simplifying the human robot interaction?
The usability of it.
[00:20:12] Speaker C: Yeah, I think the major areas are like, all our team is focused around the human robot interaction aspect.
So even the R and D engineers, when it comes to hardware things from the point of view that, okay, how the user would be interacting with this particular hardware, like what would be the touch points, where the emergency buttons or pause buttons should be. So it's very intuitive for the user. How do we even keep the form factor in such a simple fashion that it does feel like, you know, they are used to it, it's not a foreign device. Like they feel more welcoming rather than okay, this is like a weird thing. I should not touch it, I might break it. So it goes all the way from the hardware form factor in design and then a lot of development towards the vision and haptic side of things.
So my co founder, Alok is a focused engineer on the vision stack. Like, he has done several work on understanding the context and how we can train on these systems along with understanding human force feedback. Now we coupled those things to ensure that when the user is training the robot, they are taking the complexity away from it. Like things are complex, but we try to handle the complexity at our end rather than giving it to the user. So let's say if they're grabbing the robot and walking it around, our vision stack is working in parallel to see, okay, how, what it's learning from the environment and what it can use as reference points, what it can use as assets. And I think that's like the critical part that how can we do a lot of these developments that users don't have to worry about that the robot handles in house. And again, how we can do it in such a fashion that whether it's a large enterprise or a small company, both can use it. So what that means is the robot itself is a complete package. We don't need external servers to process this data. We don't transmit any data outside of the robot. So everything is happening within the form factor of the robot itself. All the compute necessary, all the sensors necessary is incorporated in the hardware itself.
[00:22:13] Speaker B: That makes sense.
So coming up to this was super helpful. So one thought that does come up. Are the robots going to take these jobs away? So the push for automation, what's the impact on the workforce? And how do you envision the balance between robotics, automation and benefits that the workforce gets out of it?
[00:22:36] Speaker C: I think the reality is that we are facing a labor shortage. Rather than replacing jobs like all the areas where we have deployed the robots so far, either the operations were something that humans cannot do and they were already using some form of different system, much more complicated, or people don't want to do that operation.
I can give a kind of like an anecdote here is that in certain sites that we have deployed these robots, almost every time, people ended up naming the robots something personal.
And the person who was previously doing that job always gets elevated to a new role. And it almost becomes their priority to ensure that the robot works because they don't want to do that job again. So it's in their interest to ensure that the robot works so that they can be elevated to this new task, which they earn more, they enjoy more. Plus, it's not that repeatable.
And that's like the core thing that we see across deployments, that people end up personalizing these robots. People end up. People who were doing that role are the biggest champions, actually. They want to make sure that these robots work. So if sometimes, let's say our robots are down, they would be the first one to call us to make sure that it's on so that they can keep focusing on their new role that they like much more than the older one that was just repeatable for them.
[00:23:58] Speaker B: No, totally makes sense. So in closing, Rishabh, what message do you have for manufacturers considering industrial 4o technology, automation, robotics, how should they. Any parting words of wisdom for them?
[00:24:15] Speaker C: I don't think kind of like I can give any wisdom from the manufacturer point of view. I think they know the best what they want to do for their operations. But one thing I always say, and I would like to highlight here, is that they should not be afraid of starting small something even from zero. I think the business models of a lot of companies, plus the technology of a lot of companies are now designed in such a way that not just about fear in general, like there are several other robotics companies that are doing this. But you can start doing something as a PoC, even if you are a very small company, you can start adopting this technology without putting a huge dent to your wallet and slowly grow it or adopt it or scale it as kind of it becomes more and more synchronous to your field or your operations. And I think a lot of times people are just afraid that maybe this won't work for me. Maybe my plans are very unique, that the robot that might be working for other manufacturers work for me.
Honestly, every time there might be something out there that would work for you. It's just that there is a need for starting and automation or robotics is a space that we can't postpone for too long. Like I think the need is today and we just want to make sure that you can start adopting it. So go out. That's the only thing I would request. Manufacturers go out. Ask these companies to start implementing something for you.
Do it at a small scale, but start doing it because otherwise it might be too late.
[00:25:46] Speaker B: Well, thanks a lot Rishabh for spending time with us. With that, this is a wrap for this episode.
Thanks a lot for your time and appreciate your insights.
[00:25:58] Speaker C: No worries Parfish. Really appreciate it. Thank you for having me. It was great to connect and really great questions.
Thank you for your time and looking forward to meeting you and interacting with you in more detail. Thank you.
[00:26:08] Speaker B: Foreign.
[00:26:13] Speaker A: Thanks for listening to INA insights. Please visit Aina AI for more podcasts, publications and events on developments shaping the industrial and industrial technology sector.
[00:26:34] Speaker C: Sam.
