MAROKO133 Breaking ai: Scientists Suggest That Igniting Oil Spills to Create Fire Tornadoe

📌 MAROKO133 Eksklusif ai: Scientists Suggest That Igniting Oil Spills to Create Fi

When you think of saving our oceans, what comes to mind? Maybe you think about reef restoration techniques like coral transplantation, or making small consumption changes like switching from plastic straws. If you have money to dream big, perhaps you might ponder creating a big robot that roams the waters, sucking up floating trash.

Environmental researchers at Texas A&M University are on a whole ‘nother level: they’re proposing massive fire tornadoes.

A recent blurb on the university’s website detailed a recent paper in the journal Fuel that aimed to find the most effective way to clean up oil spills. It involves, improbably, lighting massive “fire whirls,” the technical term for the much more evocative phrase the university used: “fire tornado.”

While it sounds counter-intuitive, the experiment explored whether fire whirls could be an effective method to help clean up the thousands of oil spills that occur each year.

The theory is pretty simple: fire whirls, spinning rapidly upward instead of outward, provide a massive boost to oil burn-offs, creating a fire that burns much hotter — and therefore much faster — than typical oil pool burn-offs. (Remarkably, the current world-standard for cleaning an oil spill is to gather a huge pool of crude on the surface and light it, a technique known as in-situ burning.)

To carry out the experiment, researchers built three 16-foot walls, situated in a loose triangle. The result was a controlled fire-tornado reaching 17-feet in height at its largest, which the scientists probed for environmental impact.

Compared to in-situ burn-offs, researchers at Texas A&M found the controlled fire whirl produced 40 percent less soot, while burning off up to 95 percent of the fuel.

“This the first time anyone has conceived using fire whirls for oil spill remediation, and it’s really just the beginning,” said Elaine Oran, professor of aerospace engineering at Texas A&M. “Our goal is to harness the chaotic nature of fire whirls as a powerful, precise restoration tool, to protect coastlines, marine ecosystems and the environment as a whole.”

Going forward, speed is one of the biggest draws inviting further research on fire whirls. “Fire whirls burn through crude oil spills nearly twice as fast as in-situ fire pools,” Oran said, “potentially giving cleanup crews faster operational and response times to eliminating the oils from spreading.”

More on pollution: Site of Elementary School Was Sprayed With Radioactive Fracking Waste, Worker Warns

The post Scientists Suggest That Igniting Oil Spills to Create Fire Tornadoes Might Actually Be Good for the Oceans appeared first on Futurism.

🔗 Sumber: futurism.com

📌 MAROKO133 Breaking ai: ByteDance Introduces Astra: A Dual-Model Architecture for

The increasing integration of robots across various sectors, from industrial manufacturing to daily life, highlights a growing need for advanced navigation systems. However, contemporary robot navigation systems face significant challenges in diverse and complex indoor environments, exposing the limitations of traditional approaches. Addressing the fundamental questions of “Where am I?”, “Where am I going?”, and “How do I get there?”, ByteDance has developed Astra, an innovative dual-model architecture designed to overcome these traditional navigation bottlenecks and enable general-purpose mobile robots.

Traditional navigation systems typically consist of multiple, smaller, and often rule-based modules to handle the core challenges of target localization, self-localization, and path planning. Target localization involves understanding natural language or image cues to pinpoint a destination on a map. Self-localization requires a robot to determine its precise position within a map, especially challenging in repetitive environments like warehouses where traditional methods often rely on artificial landmarks (e.g., QR codes). Path planning further divides into global planning for rough route generation and local planning for real-time obstacle avoidance and reaching intermediate waypoints.

While foundation models have shown promise in integrating smaller models to tackle broader tasks, the optimal number of models and their effective integration for comprehensive navigation remained an open question.

ByteDance’s Astra, detailed in their paper “Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning” (website: https://astra-mobility.github.io/), addresses these limitations. Following the System 1/System 2 paradigm, Astra features two primary sub-models: Astra-Global and Astra-Local. Astra-Global handles low-frequency tasks like target and self-localization, while Astra-Local manages high-frequency tasks such as local path planning and odometry estimation. This architecture promises to revolutionize how robots navigate complex indoor spaces.

Astra-Global: The Intelligent Brain for Global Localization

Astra-Global serves as the intelligent core of the Astra architecture, responsible for critical low-frequency tasks: self-localization and target localization. It functions as a Multimodal Large Language Model (MLLM), adept at processing both visual and linguistic inputs to achieve precise global positioning within a map. Its strength lies in utilizing a hybrid topological-semantic graph as contextual input, allowing the model to accurately locate positions based on query images or text prompts.

The construction of this robust localization system begins with offline mapping. The research team developed an offline method to build a hybrid topological-semantic graph G=(V,E,L):

• V (Nodes): Keyframes, obtained by temporal downsampling of input video and SfM-estimated 6-Degrees-of-Freedom (DoF) camera poses, act as nodes encoding camera poses and landmark references.
• E (Edges): Undirected edges establish connectivity based on relative node poses, crucial for global path planning.
• L (Landmarks): Semantic landmark information is extracted by Astra-Global from visual data at each node, enriching the map’s semantic understanding. These landmarks store semantic attributes and are connected to multiple nodes via co-visibility relationships.

In practical localization, Astra-Global’s self-localization and target localization capabilities leverage a coarse-to-fine two-stage process for visual-language localization. The coarse stage analyzes input images and localization prompts, detects landmarks, establishes correspondence with a pre-built landmark map, and filters candidates based on visual consistency. The fine stage then uses the query image and coarse output to sample reference map nodes from the offline map, comparing their visual and positional information to directly output the predicted pose.

For language-based target localization, the model interprets natural language instructions, identifies relevant landmarks using their functional descriptions within the map, and then leverages landmark-to-node association mechanisms to locate relevant nodes, retrieving target images and 6-DoF poses.

To empower Astra-Global with robust localization abilities, the team employed a meticulous training methodology. Using Qwen2.5-VL as the backbone, they combined Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO). SFT involved diverse datasets for various tasks, including coarse and fine localization, co-visibility detection, and motion trend estimation. In the GRPO phase, a rule-based reward function (including format, landmark extraction, map matching, and extra landmark rewards) was used to train for visual-language localization. Experiments showed GRPO significantly improved Astra-Global’s zero-shot generalization, achieving 99.9% localization accuracy in unseen home environments, surpassing SFT-only methods.

Astra-Local: The Intelligent Assistant for Local Planning

Astra-Local acts as the intelligent assistant for Astra’s high-frequency tasks, a multi-task network capable of efficiently generating local paths and accurately estimating odometry from sensor data. Its architecture comprises three core components: a 4D spatio-temporal encoder, a planning head, and an odometry head.

The 4D spatio-temporal encoder replaces traditional mobile stack perception and prediction modules. It begins with a 3D spatial encoder that processes N omnidirectional images through a Vision Transformer (ViT) and Lift-Splat-Shoot to convert 2D image features into 3D voxel features. This 3D encoder is trained using self-supervised learning via 3D volumetric differentiable neural rendering. The 4D spatio-temporal encoder then builds upon the 3D encoder, taking past voxel features and future timestamps as input to predict future voxel features through ResNet and DiT modules, providing current and future environmental representations for planning and odometry.

The planning head, based on pre-trained 4D features, robot speed, and task information, generates executable trajectories using Transformer-based flow matching. To prevent collisions, the planning head incorporates a masked ESDF loss (Euclidean Signed Distance Field). This loss calculates the ESDF of a 3D occupancy map and applies a 2D ground truth trajectory mask, significantly reducing collision rates. Experiments demonstrate its superior performance in collision rate and overall score on out-of-distribution (OOD) datasets compared to other methods.

The odometry head predicts the robot’s relative pose using current and past 4D features and additional sensor data (e.g., IMU, wheel data). It trains a Transformer model to fuse information from different sensors. Each sensor modality is processed by a specific tokenizer, combined with modality embeddings and temporal positional embeddi…

Konten dipersingkat otomatis.

🔗 Sumber: syncedreview.com