Robots that Know Where to Go, with George Konidaris

This week on Swarfcast, we’ve been thinking a lot about some of the exciting tech that we saw last month at IMTS. One thing that always stands out to us are the robots. As our recent podcasts have touched on the topic of AI, we thought it would be fitting to reshare an interview we did with George Konidaris, co-founder of Realtime Robotics. A new podcast is on its way soon, but in the meantime we hope you enjoy this one again.

Seems like right now every podcast is doing an interview centered around artificial intelligence.

But I waited until I found the right story, one that was truly relevant to our audience in the machining world.

Today’s guest on the podcast, George Konidaris, is the cofounder of the startup, Realtime Robotics. He is also a professor of Computer Science and the director of the Intelligent Robot Lab at Brown University.

Right now, programming a robot arm to perform a repetitive task typically requires a robot integrator to program where every joint of a robot should go. It’s a ridiculous and tedious process.

But with Realtime Robotics’ AI technology, you can instruct a robot to do a task and you don’t have to tell it a zillion steps explaining HOW to do the task.

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Interview Highlights

Noah Graff: Explain your company, Realtime Robotics.

George Konidaris: Realtime Robotics is a company that does real-time robot motion planning. We focus on how a robot can automatically generate its own motion. Typically a robot integrator programs every aspect of a robot’s motion in order to accomplish a repetitive task. This means deciding where every joint of a robot arm should go. With our system, you can tell the robot where it needs to put its business end. This is where I would like you to weld, or I would like you to pick up the object over there. We compute the rest of the motion for you.

Graff: How do you control the robots?

Konidaris: The majority of our installations are programmed using a PLC. It used to be that you would have to set every joint on the robot to a specific value.

Now instead, you can send much higher level commands to the PLC.

Graff: So it takes less training than using a typical robot controller?

It takes less training and less effort. We can reduce PLC programs that are often hundreds of statements long to single digit statements in many cases. You get out better efficiency, and we make sure there are no collisions. You don’t have to run what you’ve programmed and eyeball it to make sure it doesn’t collide.

Graff: This can integrate with all different brands?

Konidaris: Yes, we think of robot arms the way most people think of printers, which is that they’re all peripherals. Our job is to provide drivers for those peripherals. To you, they should look just the same because they have similar functionality. You don’t have to go learn the programming language associated with one robot brand. You just plug it in.

Graff: It sounds a little like ChatGPT in that it does a lot of the tedious work for you.