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.

Robots will eventually be part of every household. It is thus critical to enable algorithms to learn from and be guided by non-expert users.

City streets, sidewalks, and public areas often serve as primary interaction points among diverse user groups, including residents, commuters, and visitors ( Gehl, 2011). These spaces carry social, economic, and cultural signifi - cance that influences navigation and user experience ( Mitra ˇ sinovi c & Mehta, 2021). Municipal governments and planning agencies recognize the importance of inclusive public spaces but face challenges in operation - alizing inclusivity ( Anttiroiko & De Jong, 2020). Traditional approaches may draw on universal design principles intended to accommodate a broad range of users, but these frameworks often take a one-size-fits-all approach that prioritizes physical accessibility over the social and cul - tural dimensions of public space use ( Low, 2020). In multicultural cities, where multiple languages, cultures, and religious practices converge, these complexities become particularly evident ( Fan et al., 2023; Lit - man, 2025; Salgado et al., 2021; Youngbloom et al., 2023). Research on inclusive design has provided valuable insights, but few methods combine qualitative depth with quantitative scale to under - stand inclusivity in urban contexts ( Anttiroiko & De Jong, 2020; Mehta, 2019; Zamanifard et al., 2019). Ethnographic research and interviews offer detailed perspectives on lived experience, while computer vision and machine learning enable assessments at larger scales ( Ibrahim et al., 2020). However, large-scale computational approaches often overlook intersectional dimensions ( Zhu et al., 2025). This gap calls for integrated models that merge qualitative and quantitative methodologies.

The vulnerability of cyclists, exacerbated by the rising popularity of faster e-bikes, motivates adapting automotive perception technologies for bicycle safety. We use our multi-sensor 'SenseBike' research platform to develop and evaluate a 3D LiDAR segmentation approach tailored to bicycles. To bridge the automotive-to-bicycle domain gap, we introduce the novel BikeScenes-lidarseg Dataset, comprising 3021 consecutive LiDAR scans around the university campus of the TU Delft, semantically annotated for 29 dynamic and static classes. By evaluating model performance, we demonstrate that fine-tuning on our BikeScenes dataset achieves a mean Intersection-over-Union (mIoU) of 63.6%, significantly outperforming the 13.8% obtained with SemanticKITTI pre-training alone. This result underscores the necessity and effectiveness of domain-specific training. We highlight key challenges specific to bicycle-mounted, hardware-constrained perception systems and contribute the BikeScenes dataset as a resource for advancing research in cyclist-centric LiDAR segmentation.

Urban embodied AI agents, ranging from delivery robots to quadrupeds, are increasingly populating our cities, navigating chaotic streets to provide last-mile connectivity. Training such agents requires diverse, high-fidelity urban environments to scale, yet existing human-crafted or procedurally generated simulation scenes either lack scalability or fail to capture real-world complexity. We introduce UrbanVerse, a data-driven real-to-sim system that converts crowd-sourced city-tour videos into physics-aware, interactive simulation scenes. UrbanVerse consists of: (i) UrbanVerse-100K, a repository of 100k+ annotated urban 3D assets with semantic and physical attributes, and (ii) UrbanVerse-Gen, an automatic pipeline that extracts scene layouts from video and instantiates metric-scale 3D simulations using retrieved assets. Running in IsaacSim, UrbanVerse offers 160 high-quality constructed scenes from 24 countries, along with a curated benchmark of 10 artist-designed test scenes. Experiments show that UrbanVerse scenes preserve real-world semantics and layouts, achieving human-evaluated realism comparable to manually crafted scenes. In urban navigation, policies trained in UrbanVerse exhibit scaling power laws and strong generalization, improving success by +6.3% in simulation and +30.1% in zero-shot sim-to-real transfer comparing to prior methods, accomplishing a 300 m real-world mission with only two interventions.

The integration of electric vehicles (EVs) into smart grids presents unique opportunities to enhance both transportation systems and energy networks. However, ensuring safe and interpretable interactions between drivers, vehicles, and the surrounding environment remains a critical challenge. This paper presents a multi-modal large language model (LLM)-based framework to process multimodal sensor data - such as object detection, semantic segmentation, and vehicular telemetry - and generate natural-language alerts for drivers. The framework is validated using real-world data collected from instrumented vehicles driving on urban roads, ensuring its applicability to real-world scenarios. By combining visual perception (YOLOv8), geocoded positioning, and CAN bus telemetry, the framework bridges raw sensor data and driver comprehension, enabling safer and more informed decision-making in urban driving scenarios. Case studies using real data demonstrate the framework's effectiveness in generating context-aware alerts for critical situations, such as proximity to pedestrians, cyclists, and other vehicles. This paper highlights the potential of LLMs as assistive tools in e-mobility, benefiting both transportation systems and electric networks by enabling scalable fleet coordination, EV load forecasting, and traffic-aware energy planning. Index Terms - Electric vehicles, visual perception, large language models, YOLOv8, semantic segmentation, CAN bus, prompt engineering, smart grid.

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.

Individuals with ambulatory disabilities often encounter significant barriers when navigating urban environments due to the lack of accessible information and tools. This paper presents OmniAcc, an AI-powered interactive navigation system that utilizes GPT -4, satellite imagery, and OpenStreetMap data to identify, classify, and map wheelchair-accessible features such as ramps and crosswalks in the built environment. OmniAcc offers personalized route planning, real-time hands-free navigation, and instant query responses regarding physical accessibility. By using zero-shot learning and customized prompts, the system ensures precise detection of accessibility features, while supporting validation through structured workflows. This paper introduces OmniAcc and explores its potential to assist urban planners and mobility-aid users, demonstrated through a case study on crosswalk detection. With a crosswalk detection accuracy of 97.5%, OmniAcc highlights the transformative potential of AI in improving navigation and fostering more inclusive urban spaces.

The Eufy Lawnbot E18 is a great robot lawn mower for smaller, flatter yards and for people who don't want to spend a lot of time setting one up. Outsourcing is one of the ways manufacturers can more affordably expand their product offerings, and some smart home brands--including Anker's Eufy smart home division--have taken this approach rather than developing their own products in-house. The Eufy Lawnbot E-series robot lawn mowers that Anker debuted at CES last January are actually rebranded TerraMow models that have been available in Europe since mid-2024. Apart from battery size, the two Lawnbot E-series mowers are identical, with the model E15 capable of handling up to 0.2 acres, while the model E18 reviewed here is suitable for up to 0.3 acres. Both Lawnbot E-series mowers might seem small compared to much of the competition--suburban and rural American yards tend to be very large--but you don't need to own an acre or more of turf to appreciate a robot lawn mower. The Eufy Lawnbot E18 will look familiar to European readers, as it's based on the design of the TerraMow S2100 that came to market in that region in 2023.