Carbon Robotics Innovation, Technology & Agility

What practices does your team employ to foster innovation, and how have these practices led to more creative, out-of-the-box thinking?

It starts with the way we run embedded at Carbon: You don’t do just firmware or hardware — you get to work on every layer of the stack, from speccing parts and designing printed circuit boards to the back-end software and mobile apps that operators use. You go to the field or manufacturing plant and see how your creations do in the real world — and figure out how to make them even better. We don’t let ourselves get caught up in months-long discussions on how to build something; we just do it. The only thing we consider a failure is not trying at all. 

But most importantly, every single person working at Carbon is here not just because they want to be here, but because they believe in what we’re doing: using technology to make the world a better place. And that’s something you just won’t find at your Microsofts or Apples of the world — true, unadulterated creativity and problem-solving driven not by restricted stock units, but by genuine belief in the tomorrow we’re building.

How has a focus on innovation increased the quality of your team’s work? 

One of the core components of the LaserWeeder are the laser scanners: devices that aim the laser beams to shoot weeds. On the G2 machine, we were able to realize several firmware improvements — including squashing bugs as old as the company — on the scanners, which culminated in a two to three times massive increase in weeding speed. G2 was going two-plus miles per hour before we even delivered the first customer unit. On the other hand, G1 tops out around one mile per hour after three years of hard optimizing.

As cliché as it sounds, everything we do is innovative — I mean, who else does laserweeding? But that doesn’t mean we get complacent; we’re far from that. We all realize we’re doing what’s never been done before, and there’s no right or wrong answers. One of the greatest testaments to this has been our international expansion. For example, our European customers asked for a way to run their tractors at a lower revolution per minute — saving fuel and wear, but changing the frequency of air conditioning power — and instead of saying no to such a big change, we tested and validated this for our entire fleet in weeks.

How has a focus on innovation bolstered your team’s culture? 

Absolutely! There’s not a layer of the stack I haven’t touched, and with that, not a person on the software team I haven’t gotten to work with. From hitting the ice rink at 6 a.m. to midnight chats about cats and programming languages, I’m always thankful not only to work with such awesome people but call them my friends, too.

Can you share some examples of how AI/ML has directly contributed to enhancing your product line or accelerating time to market?

There were lots of ways we could have tackled the plant identification problem from a computer vision standpoint, but we decided early on to use deep learning, which is standard in our industry. Deep learning in particular allows us to learn directly from images without relying on any feature engineering. This allows us to quickly and constantly adapt to new plants and field conditions when we deploy a machine: instead of having to figure out exactly what makes a new weed a weed, we can simply annotate images and train a model, a process that allows us to get performance improvements out to customers more rapidly and with less effort.

We also recognize that there’s a lot of information we can gain from the data that we’ve already obtained. Detecting anomalies in weeding patterns, for example, has helped our support team determine what specific actions are likely to mitigate a customer’s issues, whereas analyzing electrical signals has helped us discover whether physical components should be replaced. Building tools to digest these massive amounts of data has allowed us to diagnose a wide range of problems like these, often before customers may realize they exist.

How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?

The main function of the LaserWeeder is to shoot weeds while protecting crops. At its core, this relies on computer vision systems to locate, categorize, track and target weeds of different shapes and sizes, and without deep learning none of this would be feasible. 

But beyond shooting weeds, the LaserWeeder is a high-powered camera-based computer that is capable of drawing insights about anything it sees. Our customers are able to view different metrics pertaining to the weeds and crops in their fields through our mobile companion app and web-based Ops Center, and our support team can use those metrics to diagnose any troubles our customers might be facing. 

As we gear towards the future, we are constantly thinking about new ways we can take advantage of the data collected by our machines to help further the experiences customers have, which includes building new data analysis tools and integrating machine learning advancements into our system that we think will address issues identified in that data.

What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?

Attending conferences has been one of the best ways to find trends that are percolating through the AI community. Similarly, reading papers and articles about recent deep learning and computer vision advancements has been a great way to learn technical details and generate new ideas that may help us improve our own systems. 

That being said, it’s imperative that we pursue ideas that are applicable to the problems we’re trying to solve. A significant portion of research today goes into pushing the limits of the next great idea, but it isn’t necessarily geared towards solving domain-specific problems on real-world datasets like the ones we’re tackling. It’s far more important that we identify ideas that can translate to our use cases rather than always chasing the next advancement, and that we validate whether ideas are promising before pursuing them on a large scale. Generally, this means that we spend a decent amount of time experimenting and prototyping before we even begin to consider how we’ll integrate an idea into production.