MAROKO133 Eksklusif ai: ByteDance Introduces Astra: A Dual-Model Architecture for Autonomo

📌 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…

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🔗 Sumber: syncedreview.com

📌 MAROKO133 Breaking ai: New battery recharges using sunlight, releases hydrogen o

A new copolymer-based battery developed by researchers at Ulm and Jena universities in Germany stores energy from sunlight for days and can release it when required as green hydrogen. The battery is rechargeable, and the charge and discharge process can be activated by flipping a pH switch, a press release said. 

With the focus on switching away from fossil fuels, countries are adopting large-scale solar and wind power plants. However, for applications requiring higher energy density, hydrogen is a more viable alternative. It can be burnt, much like a fossil fuel, but produces only water as a byproduct, offering a carbon-free solution for energy-intensive applications. 

However, hydrogen production itself can be a carbon-emitting process. Large-scale hydrogen plants use methane reforming to produce hydrogen, since it is cost-effective. For hydrogen to be an ideal replacement for fossil fuels, it must be produced using solar or wind energy, also known as green hydrogen. 

Copolymer to the rescue

Green hydrogen can be produced using sunlight through a photocatalytic process. Once the gas is produced, it needs to be stored separately in tanks and processed when required. However, a research team led by Ulrich Schubert at Jena University and Sven Rau at Ulm University decided to use copolymer molecules instead. 

Copolymers are macromolecules that consist of different organic building blocks. They have a stable framework and can be equipped with specific functional units. For this solar battery, the researchers used a water-soluble copolymer with reinforced redox activity as its chief functional unit. 

When exposed to sunlight, the system achieves 80% charging efficiency. Once charged, the system can maintain its charged state for several days. To retrieve the energy, the researchers added an acid and a hydrogen-evolution catalyst to cause the electrons stored in the system to combine with protons, thereby releasing hydrogen. Here, the system’s efficiency is high again, reaching 72 percent. 

Uses pH as a switch

The copolymer-based system features redox reactions that are completely reversible. So, when the battery is discharged, it can be left in the Sun to recharge, facilitating multiple catalytic and storage cycles. 

To reset the system, the researchers simply change its pH value. But pH is not just a switch; it is also an indicator of the polymer’s state of charge. When discharged, the presence of the acid changes the colour from violet to yellow. 

When placed in sunlight to charge, the system changes colour from yellow to violet again, showing that the battery has a charge it can release as hydrogen when necessary. The hydrogen released could be used for a wide variety of applications, from running electric cars to manufacturing steel or generating clean electricity on demand.

“The project is also of scientific significance because it combines very different concepts from the field of chemistry that otherwise have few points of contact: namely, macromolecular polymer chemistry and photocatalysis,” added Rau in the press release. 

“The results open up new perspectives for cost-effective, scalable solar storage technologies – and provide an important building block on the way to a sustainable, chemical-based energy economy,” concluded Schubert. 

The research findings were published in the journal Nature Communications. 

🔗 Sumber: interestingengineering.com