Boston Dynamics Team

Spot is more than a mobile robot; it’s a mobile sensing platform that developers can customize with software, sensors, and other hardware payloads to achieve exactly what they need for specific applications. Whether it’s building the tools to explore other planets or to inspect worksites, the ability to easily build on Spot’s base functionality has huge real-world value. 

This flexibility also opens up opportunities for more creative applications. For example, Spot has a storied history with alcoholic beverages, from inspecting breweries to doing other activities with beer. But before all that, there was Margarita Spot: an internal project developing a Spot payload that blends the perfect margarita.

It’s been a little over a year since Spot graduated from our early adopter program and was released for sale. Our early adopters helped us answer the question, “Can robots handle the real world?” Our customers’ experience with Spot over the past year has answered a follow up question, “Where is Spot adding value?”

On the surface, automated depalletizing looks relatively straightforward. A computer vision system identifies a box and then a robotic arm with a gripper lifts the box off a pallet and onto a conveyor belt. However, solving carton location and path planning to successfully unpack a pallet requires highly advanced perception and intelligent vision processing algorithms.

Over the past year, COVID-19 has put a huge strain on warehouse operations—making hiring and staffing a challenge, while simultaneously accelerating the demand for fast, effective e-commerce services. Even as social distancing restrictions lift, e-commerce adoption rates continue to rise dramatically, highlighting the need for ongoing solutions to fulfillment, shipping, and delivery challenges. The warehousing industry is primed for an automation boom to support this new landscape.

In an industrial setting, equipment failure or faulty infrastructure often requires costly unplanned downtime to repair. Process industries are looking to asset performance management (APM) and industrial predictive analytics technologies to identify impending failures, before they cause larger problems. But research from ARC Advisory Group found that 59 percent of APM system users receive less than a week notice of impending failure. This is, in part, because data capture for condition monitoring and predictive maintenance is often resource-intensive and unreliable.