Australia's beloved weather website got a makeover - and infuriated users It was an unseasonably warm spring day in Sydney on 22 October, with a forecast of 39C (99F) - a real scorcher. The day before, the state of New South Wales had reported its hottest day in over a century, a high of 44.8C in the outback town of Bourke. But little did the team at the national Bureau of Meteorology foresee that they, in particular, would soon be feeling the heat. Affectionately known by Australians as the Bom, the agency's long-awaited website redesign went live that morning, more than a decade after the last update. Within hours, the Bom was flooded with a deluge of complaints.

This paper provides an overview of the techniques employed by Hibikino-Musashi@Home, which intends to participate in the domestic standard platform league. The team developed a dataset generator for training a robot vision system and an open-source development environment running on a Human Support Robot simulator. The large-language-model-powered task planner selects appropriate primitive skills to perform the task requested by the user. Moreover, the team has focused on research involving brain-inspired memory models for adaptation to individual home environments. This approach aims to provide intuitive and personalized assistance. Additionally, the team contributed to the reusability of the navigation system developed by Pumas in RoboCup2024. The team aimed to design a home service robot to assist humans in their homes and continuously attend competitions to evaluate and improve the developed system.

In detail, we utilize foundation visual perception models to obtain each generated object from the image and leverage pre-trained depth estimation models to lift the generated 2D image to 3D space.

Finding an object in a partially known and cluttered environment is critical for many robotics tasks.

We present a novel framework for automated interior design that combines large language models (LLMs) with grid-based integer programming to jointly optimize room layout and furniture placement. Given a textual prompt, the LLM-driven agent workflow extracts structured design constraints related to room configurations and furniture arrangements. These constraints are encoded into a unified grid-based representation inspired by ``Modulor". Our formulation accounts for key design requirements, including corridor connectivity, room accessibility, spatial exclusivity, and user-specified preferences. To improve computational efficiency, we adopt a coarse-to-fine optimization strategy that begins with a low-resolution grid to solve a simplified problem and guides the solution at the full resolution. Experimental results across diverse scenarios demonstrate that our joint optimization approach significantly outperforms existing two-stage design pipelines in solution quality, and achieves notable computational efficiency through the coarse-to-fine strategy.

Reward function inputs and hyperparameters.

Is a Robot Vacuum Worth It? It's not for everyone, but sometimes my robot vacuum is my only friend. Every single day--weekend, weekday, rain or shine--whichever robot vacuum I'm currently testing starts running at 9 am. I heave a sigh of relief and continue with whatever else I was doing, content that at least f*cking chore in my house is getting done. When I first started testing robot vacuums eight years ago, it sometimes seemed like more trouble than it was worth. I cleaned up the floor .

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. Fall is for nesting--and for feathering your nest with whatever will keep you sane during the winter. Which is why a number of retailers, including The Home Depot, drop prices on home goods with big fall deals. The Home Depot fall savings event for 2025 is unusually broad, because The Home Depot itself is unusually broad--the store that first brought the home improvement superstore nationwide.

The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts. However, existing datasets typically suffer from limitations in data scale or diversity, sanitized layouts lacking small items, and severe object collisions. To address these shortcomings, we introduce \textbf{InternScenes}, a novel large-scale simulatable indoor scene dataset comprising approximately 40,000 diverse scenes by integrating three disparate scene sources, real-world scans, procedurally generated scenes, and designer-created scenes, including 1.96M 3D objects and covering 15 common scene types and 288 object classes. We particularly preserve massive small items in the scenes, resulting in realistic and complex layouts with an average of 41.5 objects per region. Our comprehensive data processing pipeline ensures simulatability by creating real-to-sim replicas for real-world scans, enhances interactivity by incorporating interactive objects into these scenes, and resolves object collisions by physical simulations. We demonstrate the value of InternScenes with two benchmark applications: scene layout generation and point-goal navigation. Both show the new challenges posed by the complex and realistic layouts. More importantly, InternScenes paves the way for scaling up the model training for both tasks, making the generation and navigation in such complex scenes possible. We commit to open-sourcing the data, models, and benchmarks to benefit the whole community.