About Ian Sherman

Ian Sherman is Head of Software at Formant, a company building cloud infrastructure for robotics. Prior to Formant, Ian led engineering teams at Google X and Bot & Dolly. The through line of his career has been tool building, for engineers and artists alike. He’s inspired by interdisciplinary collaboration of all types; currently this takes the form of applying patterns and practices from distributed systems operations to the relatively nascent field of robotics.

Links

https://formant.io

Transcript

Mike: This is the Real World DevOps Podcast and I'm your host Mike Julian. I'm setting out to meet the most interesting people doing the awesome work in the world of DevOps, from the creators of your favorite tools to the organizers of amazing conferences, from the authors of great books to fantastic public speakers. I want to introduce you to the most interesting people I can find. This episode is sponsored by the lovely folks at Influx Data. If you're listening to this podcast, you're probably also interested in better monitoring tools and that's where Influx comes in.

Personally I'm a huge fan of their products and I often recommend them to my own clients. You're probably familiar with their time series database, Influx DB, but you may not be as familiar with their other tools. Telegraf for metrics collection from systems, Chronograf for visualization and Kapacitor for real time streaming. All of these are available as open source and as a hosted SaaS solution. You can check all of that out at Influxdata.com. My thanks to Influx Data for helping make this podcast possible.

Mike: Robots. You apparently are working at some company that does observability for robots and I'm a little confused because like what in the world is this all about? Do robots actually need observability?

Ian: Yeah. I work in a company called Formant. We are about a year and a half old and we're focused on a lot of problems in supported robots, but specifically observability for robotics is very important to us. I think it's representative of the type of concern that hasn't historically been important in robotics, but is increasing as we are shipping robots more and more to customers, deploying fleets of robot, deploying them in semi-structured environments, and generally seeing their numbers increase in the wild.

Mike: These robots, are these like Johnny 5 style robots or they're more like C3PO or The Terminator or Wall-E? Are these more Wall-E or maybe even the really terrifying stuff that General Dynamics is putting out?

Ian: Right. We like to maintain a flexible definition of a robot. I think that's maybe just a way of avoiding the definition question.

Mike: I'm sure the robots in the singularity will be very happy about your loose definition.

Ian: Yeah. The vast number of deployed robots in the world has sort of traditionally defines probably in the space of automotive manufacturing. That's where we see bolted down work cells of high payload position controls, heavy metal robots performing assembly and welding and applications like that. But the fastest growing part of the robotics market is actually in service robotics and in the deployment of robotics into less structured environments. That's environments like logistics and warehousing, retail, agriculture. This is where we have started focusing is in robots in semi-structured environments.

We do think that we have a lot to offer in industrial robotics as well, but it has some better focus to date.

Mike: I saw on your website there's this really interesting photo of a robot kind of strolling down the aisle at the grocery store.

Ian: Mm-hmm (affirmative).

Mike: Is that indicative of the kind of robots we're talking about primarily?

Ian: It is. We may have a little bit of an insight into the way things are going just from the customers we're talking to every day, but we are seeing more and more robots deployed into retail for example. It's just what that image shows. The applications at the moment are typically in things like floor cleaning, inventory scanning. Those are the front of house applications that we see the most often. Of course, in order fulfillment and logistics and warehousing, we see a lot of addition applications of robotics.

Mike: Got you. I want to take a little tangent here and ask how in the world did you get into this? I don't think anyone comes out of school and says, “You know what I'm going to do? I'm going to build robots and observability.”

Ian: I came to robotics through work at a company called Bot & Dolly about seven or eight years ago. It was focused on applying robotics to challenges in film and visual effects. I had an opportunity to get involved in novel applications of industrial robotics at that company. We were acquired into Google and that was around the time that a number of robotics companies were acquired, including Boston Dynamics we mentioned. Inside Google, I had the chance to see how all of our peers were thinking about these problems. We ultimately left Google about a year and a half ago because we were excited to ship products. The timeline for that is...

Mike: There's a very subtle danger there.

Ian: The timeline is long at Google for shipping products, but the experience was really invaluable. Personally, I was already interested in the tools and infrastructure side of robotics. Through building tools to support these teams inside Google and through seeing how people thought about problems like observability, software deployment, configuration management in the context of robotics; it became clear that there's actually a huge opportunity to bring some of the best practices that have been developed for decades in the backend distributed systems world to the robotics world.

That's where I find a lot of inspiration. The problem is similar enough that we have a lot to learn, but different enough that it does require some new thinking and some new technology.

Mike: That's a really great segue into a really good question of what is it look like to do observability in robots? You mentioned all these tools and all these techniques that infrastructure people rely on every day. I can think management and that sort of thing. How is that being applied in your work?

Ian: The fundamental requirement of observability and robotics is really no different than it is in monitoring backend systems. We want to maintain visibility into the state of the system. Use that information to allow humans to respond to changes in internal systems state and also automated systems to respond to those changes. But there's a few key differences. One is that the data types that are relevant to us in robotics are often different than they are in backend distributed systems. We have sensors generating a lot of data about the physical world. Those data types are often geometric or three-dimensional or media-based.

The infrastructure and tooling to ingest and index and visualize that type of data is different. The workflows that we used to debug issues are different. They often require making sense of a lot of that geometric and visual data. Another difference is that centralizing data is often ...