Things to Do in L.A. Tap to enable a layout that focuses on the article. A delivery robot battles the elements in West Hollywood, gets support from online fans: 'Go coco, go!' Coco Robotics describes its delivery bots, pictured in 2023, as being "weather proof" and "engineered for efficient city travel." That description was put to the test during this latest storm. This is read by an automated voice. Please report any issues or inconsistencies here .

Walkability is a key component of sustainable urban development. In walkability studies, collecting detailed pedestrian infrastructure data remains challenging due to the high costs and limited scalability of traditional methods. Sidewalk delivery robots, increasingly deployed in urban environments, offer a promising solution to these limitations. This paper explores how these robots can serve as mobile data collection platforms, capturing sidewalk-level features related to walkability in a scalable, automated, and real-time manner. A sensor-equipped robot was deployed on a sidewalk network at KTH in Stockholm, completing 101 trips covering 900 segment records. From the collected data, different typologies of features are derived, including robot trip characteristics (e.g., speed, duration), sidewalk conditions (e.g., width, surface unevenness), and sidewalk utilization (e.g., pedestrian density). Their walkability-related implications were investigated with a series of analyses. The results demonstrate that pedestrian movement patterns are strongly influenced by sidewalk characteristics, with higher density, reduced width, and surface irregularity associated with slower and more variable trajectories. Notably, robot speed closely mirrors pedestrian behavior, highlighting its potential as a proxy for assessing pedestrian dynamics. The proposed framework enables continuous monitoring of sidewalk conditions and pedestrian behavior, contributing to the development of more walkable, inclusive, and responsive urban environments.

Evaluating multi-turn interactive agents is challenging due to the need for human assessment. Evaluation with simulated users has been introduced as an alternative, however existing approaches typically model generic users and overlook the domain-specific principles required to capture realistic behavior. We propose SAGE, a novel user Simulation framework for multi-turn AGent Evaluation that integrates knowledge from business contexts. SAGE incorporates top-down knowledge rooted in business logic, such as ideal customer profiles, grounding user behavior in realistic customer personas. We further integrate bottom-up knowledge taken from business agent infrastructure (e.g., product catalogs, FAQs, and knowledge bases), allowing the simulator to generate interactions that reflect users' information needs and expectations in a company's target market. Through empirical evaluation, we find that this approach produces interactions that are more realistic and diverse, while also identifying up to 33% more agent errors, highlighting its effectiveness as an evaluation tool to support bug-finding and iterative agent improvement.

'Delivery robots will happen': Skype co-founder on his fast-growing venture Starship C ity dwellers around the world have long been used to rapid delivery of takeaway food and, increasingly, groceries. But what they are not entirely used to - yet - is the sight of a robot pulling up to their front door. The co-founder of Skype, Ahti Heinla, believes his new venture is about to change that. Heinla is the chief executive of Starship Technologies, a startup that, he claimed, is able to operate deliveries run by trundling robots at a small profit - and cheaper than a human delivery driver, even in small towns and villages where delivery has not previously been viable. "We've solved everything that there is to solve," Heinla said over lunch at a London hotel.

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.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Mark Chaney filmed the Serve Robotics device repeatedly swerving into his path. This is read by an automated voice. Please report any issues or inconsistencies here . A collision between a delivery robot and a man using a mobility scooter in West Hollywood received more than 26 million views.

Sidewalk delivery robots are a promising solution for urban freight distribution, reducing congestion compared to trucks and providing a safer, higher-capacity alternative to drones. However, unreliable travel times on sidewalks due to pedestrian density, obstacles, and varying infrastructure conditions can significantly affect their efficiency. This study addresses the robust route planning problem for sidewalk robots, explicitly accounting for travel time uncertainty due to varying sidewalk conditions. Optimization is integrated with simulation to reproduce the effect of obstacles and pedestrian flows and generate realistic travel times. The study investigates three different approaches to derive uncertainty sets, including budgeted, ellipsoidal, and support vector clustering (SVC)-based methods, along with a distributionally robust method to solve the shortest path (SP) problem. A realistic case study reproducing pedestrian patterns in Stockholm's city center is used to evaluate the efficiency of robust routing across various robot designs and environmental conditions. The results show that, when compared to a conventional SP, robust routing significantly enhances operational reliability under variable sidewalk conditions. The Ellipsoidal and DRSP approaches outperform the other methods, yielding the most efficient paths in terms of average and worst-case delay. Sensitivity analyses reveal that robust approaches consistently outperform the conventional SP, particularly for sidewalk delivery robots that are wider, slower, and have more conservative navigation behaviors. These benefits are even more pronounced in adverse weather conditions and high pedestrian congestion scenarios.

Breakthroughs, discoveries, and DIY tips sent every weekday. Subway commuters in Shenzhen, China, may soon need to make room for a fleet of chunky, snack-carrying delivery robots. Earlier this week, more than three dozen autonomous, four-wheeled delivery robots boarded and exited active subway trains, and eventually delivered packages to several 7-Eleven convenience stores. Although this demonstration was only a preliminary test and took place during off-peak hours, the company behind the subway-riding robots believes they could soon help stock shelves at around 100 7-Eleven locations. The initiative is part of a broader effort in China and other countries to normalize the presence of delivery robots operating in public spaces.

Today's Internet of Things (IoT) has evolved from simple sensing and actuation devices to those with embedded processing and intelligent services, enabling rich collaborations between users and their devices. However, enabling such collaboration becomes challenging when transient devices need to interact with host devices in temporarily visited environments. In such cases, fine-grained access control policies are necessary to ensure secure interactions; however, manually implementing them is often impractical for non-expert users. Moreover, at run-time, the system must automatically configure the devices and enforce such fine-grained access control rules. Additionally, the system must address the heterogeneity of devices. In this paper, we present CollabIoT, a system that enables secure and seamless device collaboration in transient IoT environments. CollabIoT employs a Large language Model (LLM)-driven approach to convert users' high-level intents to fine-grained access control policies. To support secure and seamless device collaboration, CollabIoT adopts capability-based access control for authorization and uses lightweight proxies for policy enforcement, providing hardware-independent abstractions. We implement a prototype of CollabIoT's policy generation and auto configuration pipelines and evaluate its efficacy on an IoT testbed and in large-scale emulated environments. We show that our LLM-based policy generation pipeline is able to generate functional and correct policies with 100% accuracy. At runtime, our evaluation shows that our system configures new devices in ~150 ms, and our proxy-based data plane incurs network overheads of up to 2 ms and access control overheads up to 0.3 ms.