AI News, Brad Porter, VP of Robotics at Amazon, on Warehouse Automation, Machine Learning, and His First Robot

Brad Porter, VP of Robotics at Amazon, on Warehouse Automation, Machine Learning, and His First Robot

Starting with its acquisition of Kiva Systems for $775 million back in 2012, Amazon has been steadily investing in a robotic future.

We’re not always sure that they’re going about it the right way, but we are always in favor of companies with as much clout as Amazon has recognizing that robotics is worth focusing on, especially with an understanding that some problems are going to take years of work to solve.

He later joineda team led byJeff Wilke,chief executive of worldwide consumer,as a distinguished engineer, and during that time he oversaw technical preparations for Amazon’s first Prime Day and helped establishthe Prime Air drone delivery organization.Porterearned bachelor’s and master’s degreesin computer science at MIT before joining Netscape and later helping start an early cloud technology company.

This isn’t specific to the robotics team but the culture at Amazon as a whole is unique in the way it operates and encourages people to be curious, look around corners and fail fast.

In the robotics group, that willingness to be curious, look around corners and fail quickly is allowing us to embrace emerging technology and techniques, such as AI-based controls.

The team also has made them larger in size so they are able to move actual pallets of product in addition to the smaller drive units that move inventory pods.

The combination of high-speed wireless networks, batteries, higher-quality sensors at lower-costs and more compute horsepower in smaller packages allows us to rethink traditional solutions, like fixed conveyor belts with more flexible solutions like robotic drives, while allowing us to more flexibly rethink how processes work.

It’s great if we test a robot in a small environment and it works perfectly, but we need it to also work in a larger environment without disrupting the entire fulfillment process.

We’ve been playing with these techniques and showed a fun little demonstration at our MARS conference last year where our robotic arm learned to play beer pong just using feedback from humans as to whether it was getting better or worse.

Our systems rely on having knowledge of where every item you might want to buy is stored, but sometimes items end up stored in the wrong place, or they fall out.

By combining the photo with knowledge of what is supposed to be in every slot, we then use machine learning to assign a probability that a slot might not have the right inventory—maybe it looks too full or not full enough or the wrong colors.

This technique isn’t perfect because we’re only seeing the front of the pod and not everything in the shelf, but by using those predictions, we’ve been able to identify and correct misplaced inventory with less manual counting.

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