On this episode of, we introduce you to DoorDash's new delivery robot and discuss what the growing robot population means for humans. Coco delivery robots navigate the streets of Santa Monica, CA. All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. Earlier this week, DoorDash unveiled its own new autonomous robot called Dot. The company says it's part of its goal to have a "hybrid" model for deliveries. It's the latest sign of a renewed interest in the industry of delivery robots after years of challenges. WIRED's Aarian Marshall joins us to discuss why this matters for all of us, whether we're ordering in or not. Write to us at uncannyvalley@wired.com . You can always listen to this week's podcast through the audio player on this page, but if you want to subscribe for free to get every episode, here's how: If you're on an iPhone or iPad, open the app called Podcasts, or just tap this link . Hey, Louise, how are you doing? Yeah, Lauren is on a really exciting trip to Arizona that I'm sure we'll hear more about soon. So, as her editor, I am happy to fill in when she's off on an adventure.

The hype around delivery robots has fizzled, but DoorDash is still determined to launch Dot, an adorable red bot. It can ride on roads and in bike lanes, where it will face daunting challenges. DoorDash's new delivery robot is named Dot. When we first got close to Dot, DoorDash's new delivery robot, we looked right into its big blue, pixelated eyes and gave it a kick. It's not WIRED's policy to be mean to 350-pound hunks of plastic on wheels.

Realistic visual renderings of street-design scenarios are essential for public engagement in active transportation planning. Traditional approaches are labor-intensive, hindering collective deliberation and collaborative decision-making. While AI-assisted generative design shows transformative potential by enabling rapid creation of design scenarios, existing generative approaches typically require large amounts of domain-specific training data and struggle to enable precise spatial variations of design/configuration in complex street-view scenes. We introduce a multi-agent system that edits and redesigns bicycle facilities directly on real-world street-view imagery. The framework integrates lane localization, prompt optimization, design generation, and automated evaluation to synthesize realistic, contextually appropriate designs. Experiments across diverse urban scenarios demonstrate that the system can adapt to varying road geometries and environmental conditions, consistently yielding visually coherent and instruction-compliant results. This work establishes a foundation for applying multi-agent pipelines to transportation infrastructure planning and facility design.

We describe a method for modeling spatial context to enable video anomaly detection. The main idea is to discover regions that share similar object-level activities by clustering joint object attributes using Gaussian mixture models. We demonstrate that this straightforward approach, using orders of magnitude fewer parameters than competing models, achieves state-of-the-art performance in the challenging spatial-context-dependent Street Scene dataset. As a side benefit, the high-resolution discovered regions learned by the model also provide explainable normalcy maps for human operators without the need for any pre-trained segmentation model.

I saw two visions of the future in Los Angeles last weekend. First, a Waymo Jaguar I-PACE pulled over to pick me up on a busy street in downtown L.A., spinning lidar sensors mounted on the hood like a second set of side mirrors. We inched comfortably through stop-and-go Saturday afternoon traffic and made an impressive left turn ahead of two lanes of oncoming cars as I said my prayers in the passenger seat. On the other hand, the robot lost its nerve trying to turn right across a crosswalk. As pedestrians cleared and the light turned from green to yellow to red, the Waymo remained fixed to the spot.

Cyclists prefer to use infrastructure that separates them from motorized traffic. Using a traffic light to segregate car and bike flows, with the addition of bike-specific green phases, is a lightweight and cheap solution that can be deployed dynamically to assess the opportunity of a heavier infrastructure such as a separate bike lane. To compensate for the increased waiting time induced by these new phases, we introduce in this paper a deep reinforcement learning solution that adapts the green phase cycle of a traffic light to the traffic. Vehicle counter data are used to compare the DRL approach with the actuated traffic light control algorithm over whole days. Results show that DRL achieves better minimization of vehicle waiting time at almost all hours. Our DRL approach is also robust to moderate changes in bike traffic. The code of this paper is available at https://github.com/LucasMagnana/A-DRL-solution-to-help-reduce-the-cost-in-waiting-time-of-securing-a-traffic-light-for-cyclists.

Value alignment is essential for building AI systems that can safely and reliably interact with people. However, what a person values--and is even capable of valuing--depends on the concepts that they are currently using to understand and evaluate what happens in the world. The dependence of values on concepts means that concept alignment is a prerequisite for value alignment--agents need to align their representation of a situation with that of humans in order to successfully align their values. Here, we formally analyze the concept alignment problem in the inverse reinforcement learning setting, show how neglecting concept alignment can lead to systematic value mis-alignment, and describe an approach that helps minimize such failure modes by jointly reasoning about a person's concepts and values. Additionally, we report experimental results with human participants showing that humans reason about the concepts used by an agent when acting intentionally, in line with our joint reasoning model. People's thoughts and actions are fundamentally shaped by the concepts they use to represent the world and formulate their goals.

Argo tests in multiple cities to ensure its SDS is exposed to a wide range of driving regulations, enabling it to operate appropriately and consistently with local rules, which often vary from place to place. Consider, for example, how a vehicle should behave when turning right if there is a bike lane. In California, a car may occupy the bike lane to turn right on red, but in Pennsylvania, the same right turn requires the car to stay in the vehicle lane. Argo's powerful prediction system can incorporate a database of driving styles from which to match data, anticipate likely actions, make appropriate decisions, and avoid extreme situations in order to achieve "naturalistic driving." The SDS can even handle the (in)famous "Pittsburgh left," an unwritten rule in Argo's home city which calls for oncoming traffic to give up the right-of-way and politely let left-turning vehicles turn against a green.

We've been so worried about whether AI-driven robots will take our jobs that we forgot to ask a much more basic question: will they take our bike lanes? That's the question Austin, Texas, is currently grappling with, and it points to all sorts of unresolved issues related to AI and robots. As revealed in Anaconda's State of Data Science 2021 report, the biggest concern data scientists have with AI today is the possibility, even likelihood, of bias in the algorithms. Leave it to Austin (tagline: "Keep Austin weird") to be the first to have to grapple with robot overlords taking over their bike lanes. If a robot that looks like a "futuristic ice cream truck" in your lane seems innocuous, consider what Jake Boone, vice-chair of Austin's Bicycle Advisory Council, has to say: "What if in two years we have several hundred of these on the road?" If this seems unlikely, consider just how fast electric scooters took over many cities.

In a narrow glimpse of the increasingly automated future awaiting humanity, 10 silver robots shaped like ice-cream carts are delivering Southside Flying Pizza to hungry Austinites in Travis Heights and the Central Business District. The company behind the three-wheeled machines is hoping to grow its fleet exponentially and be part of a technological revolution in how people receive their deliveries. "Robots are your friends," said Luke Schneider, CEO of Michigan-based Refraction AI. "Robots are going to make your life more convenient. They're going to make your city more sustainable, and they're going to make your life better." The battery-powered devices, called REV-1s, go up to 15 mph and can recognize traffic lights and signs.