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

📌 MAROKO133 Eksklusif ai: ByteDance Introduces Astra: A Dual-Model Architecture fo

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

📌 MAROKO133 Update ai: Perseverance makes first-ever detection of crackling ‘mini-

Mars, long thought to be eerily silent, may be more electric than we ever imagined.

NASA’s Perseverance rover has recorded dozens of tiny electrical zaps, a possible sign that the Red Planet’s dusty atmosphere is electrically alive.

That’s not thunder you hear, but the crackle of “mini-lightning” dancing among dust devils and storms.

For decades, scientists have speculated that Martian dust storms might spark electrical discharges, but proof remained elusive.

Now, researchers led by Baptiste Chide from the Institute for Research in Astrophysics and Planetology in Toulouse say they’ve captured the first concrete evidence.

Their findings draw on 28 hours of microphone recordings from Perseverance taken over two Martian years. In that time the team identified 55 distinct electrical-discharge events tied to strong winds, dust devils, and storm fronts.

Most of the discharges clustered within the top 30 percent of wind events recorded; 16 occurred during two close encounters with dust devils.

Dust-spark signals

Sparks were often within just a few centimetres of the rover’s microphone, making the zaps audible, but too faint to meet our Earth-style definition of lightning.

This subtle dance of electricity, triggered by shifting sand and dust, is known as triboelectricity. On Earth, it can produce small shocks when you touch a metal doorknob after walking on a carpet.

On Mars, the thin carbon dioxide atmosphere makes electrical arcs far easier to produce.

Dust storms and swirling dust devils have long been suspected to carry electrical energy. But until now, nobody had heard or captured anything that sounded like it on Mars or anywhere else.

The new recordings indicate that Mars may experience faint, frequent sparks rather than dramatic lightning bolts. “It sounded like a spark or whip-crack,” said co-author Ralph Lorenz.

The implications are far-reaching. According to Chide, “These discharges represent a major discovery, with direct implications for Martian atmospheric chemistry, climate, habitability and the future of robotic and human exploration.”

Electrostatic discharges could drive chemical reactions in the Martian soil and atmosphere, potentially altering surface chemistry or affecting the preservation of organic molecules. They could also present hazards for future equipment or human missions on the Red Planet.

Small sparks, big questions

Researchers emphasize that while this isn’t Earth-style lightning, the discovery unlocks a new dimension of Martian weather.

Still, the evidence so far is audio and electromagnetic signals only; no visual flashes or optical data have been recorded.

The team calls for more dedicated instruments and better atmospheric models to quantify how widespread and frequent these discharges might be, and what they might mean for Mars’ climate, dust transport, and chemical landscape.

This study appears in the journal Nature.

🔗 Sumber: interestingengineering.com