Millions of people who spent years chasing virtual Pokémon through city streets unknowingly helped create one of the largest real-world datasets now being used to train robots.

Niantic Spatial, a company spun out of the augmented reality developer behind Pokémon Go, has partnered with Coco Robotics to improve navigation for urban delivery robots. The collaboration uses spatial mapping data collected from players of Niantic’s games to help robots move through complex city environments.

The project highlights an unexpected intersection between gaming, artificial intelligence, and robotics: the same technology used to place digital creatures on a phone screen can also guide autonomous vehicles through real-world streets.

Turning AR Gameplay into Spatial Intelligence

When Pokémon Go launched in 2016, millions of players explored cities while using their smartphones to capture virtual creatures layered onto real-world environments.

Behind the scenes, the game relied on Niantic’s Visual Positioning System (VPS), a technology designed to understand a user’s location by analyzing surrounding landmarks rather than relying solely on GPS signals.

Players contributed to this system by scanning buildings, monuments, and other public spaces from different angles using their phones.

Over time, these scans created detailed three-dimensional maps of real-world locations.

The data helped Niantic improve AR accuracy in its games, but it also built a massive spatial dataset describing how cities look from ground level – exactly the type of information robots need to navigate sidewalks and intersections.

The Same Problem as Catching Pikachu

Niantic Spatial now aims to apply that dataset to robotics.

The company’s first robotics partnership is with Coco Robotics, which operates a fleet of small autonomous delivery robots designed to transport food and groceries through city streets.

Coco’s robots currently operate in several cities, including Los Angeles, Chicago, Miami, Jersey City, and Helsinki.

Navigating urban environments is one of the hardest problems in robotics. Tall buildings interfere with GPS signals, sidewalks are crowded with pedestrians, and conditions change constantly.

Niantic’s VPS technology helps solve this problem by allowing robots to identify their exact location by comparing camera images to its spatial database of landmarks.

According to Niantic Spatial CEO John Hanke, the underlying technical challenge is surprisingly similar to what players experienced in the AR game.

In both cases, software must understand how objects move through a complex physical world.

A Dataset Built by Millions of Players

Much of the data powering the system was collected indirectly by players.

Niantic introduced features that encouraged users to photograph and scan locations in exchange for rewards within the game, such as items or rare Pokémon.

These contributions helped build a detailed visual model of cities under different lighting conditions, weather, and viewing angles.

While Niantic has long acknowledged that its games collect environmental data, the revelation that these datasets are now helping train robots may surprise some players.

Still, for robotics developers, such datasets are extremely valuable.

Unlike simulation environments or small-scale robotics experiments, Niantic’s data reflects the messy complexity of real-world cities.

Gaming Data Meets Urban Robotics

The collaboration illustrates a broader trend in robotics development: companies are increasingly relying on large-scale datasets collected outside traditional robotics research.

Consumer technologies – including smartphones, cameras, and games – are generating vast amounts of real-world visual information that can help train autonomous systems.

For delivery robots attempting to navigate dense urban environments, that data may prove critical.

If the partnership succeeds, the hours players spent exploring parks, sidewalks, and landmarks in search of digital creatures may end up helping robots find their way through the same streets.

In other words, catching Pikachu may have helped teach a robot how to deliver pizza.

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A humanoid robot operating in a residential neighborhood in Macau was escorted away by police after startling an elderly pedestrian, an incident that has sparked debate about how robots should operate in public spaces.

The episode occurred near a housing complex in the Patane district when a Unitree G1 humanoid robot was reportedly following behind a woman walking along the street. According to local reports, the 70-year-old pedestrian became alarmed after noticing the robot behind her while she was checking her phone.

A video circulating online shows the woman confronting the robot while several onlookers watch. Police later arrived at the scene and escorted the robot away from the area.

The woman was later taken to a hospital after reporting that she felt unwell. Authorities said she was examined and discharged, and that no physical contact or injuries occurred.

A Promotional Robot in the Wrong Place

Local officials said the robot belonged to an educational center and had been used for promotional activities in the neighborhood.

According to representatives from the organization operating the machine, the encounter was accidental. The woman had stopped in the middle of the walkway while looking at her phone, and the robot – unable to navigate around her – remained stationary behind her until she turned around and noticed it.

The robot’s illuminated sensors and late-evening timing may have contributed to the surprise.

Police returned the machine to its operator and advised him to exercise greater caution when using robots in public areas.

Public Robots and Social Reactions

The incident quickly spread online after video footage showed officers walking the humanoid robot away from the scene in what many viewers described as an unusual “perp walk”.

The moment triggered widespread discussion across social media platforms.

Some observers treated the situation humorously, joking that the robot had been “arrested”. Others used the moment to raise more serious questions about safety, consent, and etiquette when robots interact with people in public environments.

Humanoid robots such as the Unitree G1 have become increasingly visible in Chinese cities and online videos, where they are often used for demonstrations, entertainment, and social media content.

However, their presence in everyday public spaces remains relatively new.

The Challenge of Robots in Public Spaces

As robotics technology becomes more accessible, incidents like the Macau encounter highlight the growing need for guidelines governing how machines operate in shared environments.

Unlike industrial robots or delivery robots that follow defined paths, humanoid robots can move through public spaces designed for human interaction.

That flexibility raises new questions about awareness, human perception, and social expectations.

Even when operating safely, robots can surprise or confuse people who are not accustomed to encountering autonomous machines in everyday settings.

Researchers studying human-robot interaction note that designing robots that behave predictably – and communicate their intentions clearly – will be essential as machines become more visible in public life.

For now, the Macau incident serves as a reminder that the social side of robotics may be just as important as the technical one.

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Amazon-owned autonomous vehicle company Zoox is expanding its robotaxi testing program to Phoenix, Arizona, and Dallas, Texas, as the company continues building toward commercial deployment of its purpose-built autonomous vehicles.

The expansion will introduce Zoox’s autonomous driving technology into two additional urban environments while also supporting the launch of new operational infrastructure, including fleet depots and a new operations center in Scottsdale, Arizona.

With these additions, Zoox now operates testing fleets across ten major U.S. markets, reflecting a broader effort by autonomous vehicle developers to gather real-world data across diverse driving conditions.

Testing in New Environments

The first phase of Zoox’s rollout in Phoenix and Dallas will involve a small number of retrofitted SUVs used for mapping and early testing.

These vehicles will initially be driven manually as engineers map city streets and gather environmental data. Autonomous testing will follow, with safety drivers remaining behind the wheel to intervene if necessary.

Once the company completes this phase, Zoox plans to deploy its purpose-built robotaxi vehicles in both cities.

Each location presents unique testing conditions. Phoenix offers an opportunity to evaluate sensor performance and vehicle durability in extreme heat and dusty environments, particularly on high-speed roads common in the region.

Dallas, meanwhile, provides a complex road network and more variable weather patterns, helping engineers refine how the autonomous system handles diverse driving scenarios.

A Partnership with Uber

At the same time, Zoox is expanding its distribution strategy through a new partnership with Uber.

Under a multi-year agreement, Zoox robotaxis will be integrated into Uber’s ride-hailing platform, allowing users to request autonomous rides through the Uber app in selected cities.

The first integration is expected to begin in Las Vegas later this year, followed by Los Angeles in 2027.

Zoox will continue offering rides through its own mobile application as well, effectively operating on both its proprietary platform and Uber’s global network.

The partnership reflects Uber’s strategy of collaborating with autonomous vehicle developers rather than building its own driverless technology.

Uber previously ran an in-house autonomous vehicle program but sold the division after a fatal crash in 2018. Since then, the company has shifted toward forming partnerships with technology developers.

Building the Infrastructure for Autonomous Fleets

Supporting Zoox’s growing robotaxi program is a network of facilities known as Fusion Centers.

The company is opening a third such facility in Scottsdale, Arizona, joining existing centers in Las Vegas and the San Francisco Bay Area.

These facilities function as operational command centers, coordinating autonomous fleets through teleoperations, mission control, and rider support systems.

Fusion Centers allow human operators to assist vehicles in complex scenarios, manage fleet operations, and provide customer service for passengers.

Since launching its early robotaxi service in Las Vegas and testing programs in San Francisco, Zoox says its vehicles have completed more than one million autonomous miles and transported over 300,000 passengers.

The company’s robotaxi design differs from traditional vehicles. The fully autonomous platform eliminates the steering wheel and pedals, replacing them with a bidirectional cabin featuring face-to-face seating intended to encourage social interaction among riders.

The Growing Robotaxi Race

Zoox’s expansion highlights the intensifying competition among companies seeking to deploy autonomous ride-hailing services.

Developers such as Waymo, Cruise, and several emerging startups are all testing driverless vehicles across multiple U.S. cities, racing to demonstrate safe and scalable operations.

For Zoox, the strategy combines purpose-built vehicles, extensive real-world testing, and partnerships with major mobility platforms.

As autonomous driving technology moves from pilot programs toward commercial deployment, cities like Phoenix and Dallas are becoming critical testing grounds for the next phase of driverless transportation.

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