Most existing robotic manipulation benchmarks focus on simplified tabletop scenarios, typically involving a stationary robotic arm interacting with various objects on a flat surface. To address this limitation, we introduce RoboBench-Mart, a more challenging and realistic benchmark designed for dark store environments, where robots must perform complex manipulation tasks with diverse grocery items. This setting presents significant challenges, including dense object clutter and varied spatial configurations -- with items positioned at different heights, depths, and in close proximity. By targeting the retail domain, our benchmark addresses a setting with strong potential for near-term automation impact. We demonstrate that current state-of-the-art generalist models struggle to solve even common retail tasks. To support further research, we release the RoboBenchMart suite, which includes a procedural store layout generator, a trajectory generation pipeline, evaluation tools and fine-tuned baseline models.

If you've ever researched a topic online only to be presented with an ad for a related product seconds later, you've experienced the power of artificial intelligence in the retail industry. Most consumers know AI is playing an ever-larger role in enticing us to make purchases--it's a level of personalization that can be welcome at times (and a little alarming at others). But retailers are using AI for far more than sending out email promotions or displaying timely online ads. AI is helping the retail industry track consumers' behavior both online and in stores; guiding retailers in making smarter decisions when it comes to pricing, inventory levels and store layouts; and much more. Below, 15 members of Forbes Technology Council explain some of the ways retail businesses leverage AI that many consumers don't know about (but should).

Three QUT researchers are part of an international research team that have identified new ways for retailers to use Artificial Intelligence in concert with in-store cameras to better capture consumer behavior and tailor store layouts to maximize sales. In research published in Artificial Intelligence Review, the team propose an AI-powered store layout design framework for retailers to best take advantage of recent advances in AI techniques, and its sub-fields in computer vision and deep learning to monitor the physical shopping behaviors of their customers. Any shopper who has retrieved milk from the farthest corner of a shop knows well that an efficient store layout presents its merchandise to both attract customer attention to items they had not intended to buy, increase browsing time, and easily find related or viable alternative products grouped together. A well-thought-out layout has been shown to positively correlate with increased sales and customer satisfaction. It is one of the most effective in-store marketing tactics that can directly influence customer decisions to boost profitability.

In research published in Artificial Intelligence Review, the team propose an AI-powered store layout design framework for retailers to best take advantage of recent advances in AI techniques, and its sub-fields in computer vision and deep learning to monitor the physical shopping behaviours of their customers. Any shopper who has retrieved milk from the farthest corner of a shop knows well that an efficient store layout presents its merchandise to both attract customer attention to items they had not intended to buy, increase browsing time, and easily find related or viable alternative products grouped together. A well thought out layout has been shown to positively correlate with increased sales and customer satisfaction. It is one of the most effective in-store marketing tactics which can directly influence customer decisions to boost profitability. QUT researchers Dr Kien Nguyen and Professor Clinton Fookes from the School of Electrical Engineering & Robotics and Professor Brett Martin, QUT Business Schoolteamed up with researchers Dr Minh Le, from the University of Economics, Ho Chi Minh city, Vietnam, and Professor Ibrahim Cil from Sakarya University, Serdivan, Turkey, to conduct a comprehensive review on existing approaches to in store layout design.

So, just how is AI improving the retail experience? Like most men in the world, shopping (especially shopping for clothes) is an unpleasant experience for me, as I really feel like my time could be much better utilized. I make it a personal mission to get in and out in minimal time. So, I wonder to myself, "Could AI make my experience better?" But I think there's a lot of potential for retailers to use AI to improve my shopping experience and make sure that I find what I need as quickly as possible, ensuring that I shop more frequently.

Way back in 2017, retail industry analysts predicted a "retail apocalypse" due to the number of brick-n-mortar retailers that closed their doors. So far, however, the four horsemen of 2018 have not yet been summoned. Today, more businesses than ever are leveraging artificial intelligence (AI), augmented reality (AR), and virtual reality (VR) in retail to find new ways to attract and engage customers. Here are just a few ways retailers use mobile and emerging technologies to commingle brick-n-mortar and digital experiences. Consider the explosive growth of interactive voice technologies like Alexa, Google Home and Siri.

Across the globe, in millions of storefronts, employees build displays and arrange products in a bid to get customers to buy things they hadn't planned on. It's called a store plan, and thanks to the marriage of AI data analysis and virtual reality the concept is much smarter than it's ever been. These plans are traditionally conceived in warehouses, giant physical spaces mocked up to resemble the interior of a store. This seems like a huge waste, now that technology is making it more feasible to create a virtual environment to tinker in. InContext Solutions has a better idea: get rid of the building, recreate the storefront in VR, and put AI on the shelves to aid in the creation of smarter store layouts.

The data which may be messy, unwieldy and big. Can be used for feature creation. For Example: I had a use case where I had a graph of 60 Million customers and 130 Million accounts. Each account was connected to other account if they had the Same SSN or Same Name DOB Address. I had to find customer ID's for each of the accounts.