RoboBusiness 2024 recap

The Robot Report Podcast · 19 Oct 2024 · 51m

AI in practice: 4 real uses of AI described

What this episode covers

Steve Crowe, Gene Demaitre, and Mike Oitzman of The Robot Report recap RoboBusiness 2024, highlighting the launch of Robust AI's Carter Pro AMR and keynotes from Rodney Brooks and Sergey Levine. The hosts discuss the industry's shift toward practical utility, noting the prominence of outdoor robotics and the cooling of humanoid hype, while also reviewing the Startup Pavilion winner, Robotics 88. They emphasize the value of networking and the transition from novelty to profitable automation solutions in the warehouse sector.

AI in practice

Where this conversation describes AI being used to build or deliver a product. Part of the AI in practice tracker.

Engineering & simulationRobust AI Carter Pro

The Carter Pro AMR uses AI neural nets running on onboard compute to enable true human-robot collaboration, allowing operators to physically push and steer the cart while the system dynamically adjusts inertia and direction for fluid motion.

“we put a, what we call the hat, which has got lots of cameras and compute and AI, uh, neural nets running all the time on top. … the real innovation that makes the robots so much better is that you can push it sideways, with your own strength, without using the handleback.”

Rodney Brooks · Hear it at 19:10

Engineering & simulationRobust AI Carter Pro

The system uses AI software to dynamically light up specific totes on the cart based on scanned box dimensions, simplifying the 'put-to-light' picking process for warehouse operators.

“when they scan the box. To put it on the cart. It knows how big that code is, so it knows how exactly how much lighting to light up later. … that's all taken care of by the, you know, background. You know, fairly traditional, simple AI software.”

Rodney Brooks · Hear it at 24:40

Engineering & simulationPhysical Intelligence (Foundation Models)

Research indicates that foundation models allow robots to generalize across multiple tasks and learn them faster than models trained on specific tasks, improving overall effectiveness.

“with the general models, they can train robots to be actually more effective. And doing multiple tasks, then with models hurt the specific tasks. And once that sort of sung in, like, oh, that means that, potentially, robots can generalize and understand multiple tasks and learn them faster than they do currently.”

Gene Dimitri · Hear it at 26:32

Engineering & simulationBlue River Technology (John Deere)

Blue River Technology trained AI models to identify and distinguish between different species of plants, a core component of their precision agriculture technology.

“we learned from Chris Padwick at Blue River and John Deere about how they have trained all of the different species of plants, right? So all of that's a core part of developing AI.”

Mike Oitsman · Hear it at 43:15

Mentioned

Companies
Robust AI, Physical Intelligence, Tesla, Waymo, ABB, Nvidia, DHL, Teradyne, Honda, iRobot, Rethink Robotics, Formant, Open Droids, Robotics 88, Summer Robotics, Paradigm Robotics Ventures, Zebra Robotics Automation, Kiva Systems, Locust Robotics, Foxglove, Blue River Technology, John Deere
Products
Carter Pro, Optimus, Asimo, Burrow, Canvas, Zebra Symmetry Fulfillment
Also
Rodney Brooks, Sergey Levine

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