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

📌 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…

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🔗 Sumber: syncedreview.com

📌 MAROKO133 Update ai: 7 of the world’s most powerful tidal turbines generating me

Tides are predictable years in advance, unlike wind or sunlight. Because of this reliability, harnessing the energy of ocean tides has long appealed to engineers. The difficulty lies in building machines large enough to capture meaningful amounts of power while surviving one of the harshest environments on Earth. 

Only a handful of tidal turbines have reached megawatt-scale capacity, and even fewer have delivered sustained, grid-connected performance at sea. This list highlights some of the largest tidal turbines developed to date, including both operational systems and landmark prototypes that shaped today’s technology.

1) Orbital O2 – 2 MW Floating Tidal Turbine, Scotland, UK

The Orbital O2 is widely regarded as the most powerful operational tidal turbine currently in service. Commissioned in July 2021, this floating tidal generator is installed at the European Marine Energy Centre (EMEC)’s Fall of Warness test site off the Orkney Islands and is grid-connected via subsea cable to the local electricity network. 

It features a 2 MW nameplate capacity, achieved through two 1 MW rotors mounted on a 242 feet (74 meter) floating superstructure, which is moored in fast-flowing tidal currents. A key innovation is its retractable leg design, which provides surface maintenance access and reduces the need for heavy vessels. 

The 65 feet (20 meter) diameter rotors provide a large swept area for capturing tidal kinetic energy at speeds exceeding 3 m/s, and the device is expected to operate for up to 15 years. Building on earlier prototypes, O2’s success demonstrates that large floating tidal turbines can reliably deliver predictable renewable power into a real grid.

2) ScotRenewables SR2000 – 2 MW Floating Tidal Turbine, Scotland, UK

The SR2000 was a pioneering 2 MW floating tidal turbine developed by ScotRenewables Tidal Power (now Orbital Marine Power) and tested at EMEC’s Fall of Warness site starting in late 2016. As a full-scale prototype, it was engineered to demonstrate utility-class tidal energy generation and successfully operated in harsh North Atlantic conditions. 

Over its testing programme in 2017-2018, the SR2000 achieved full-rated output, exported power to the local grid, and generated in excess of 3 GWh of renewable electricity over approximately 12 months, a level of output that exceeded the cumulative generation previously recorded across Scotland’s wave and tidal sector. It also endured sea states with waves over 13 feet (4 meters) and maintained generation power during winter storms. 

At times, it supplied up to 25 percent of the Orkney Islands’ electricity demand during continuous generation periods. The machine was removed in September 2018 to make way for the next-generation Orbital O2 turbine, marking the SR2000 as a historic milestone in tidal turbine engineering.

3) SIMEC Atlantis AR2000 – 2 MW-Rated Single-Rotor Tidal Turbine (Design Context)

The SIMEC Atlantis AR2000 is a 2 MW-rated tidal turbine design representing one of the largest single-rotor platforms promoted in the tidal energy sector. Developed by SIMEC Atlantis Energy, the AR2000 was unveiled with design specifications targeting 2 MW output, and its scale positions it among the highest-capacity individual tidal turbines proposed. 

While not yet widely deployed as a grid-connected, operational single unit at the time of reporting, SIMEC Atlantis highlighted the AR2000’s large rotor diameter and output potential as a next step in scaling tidal stream energy beyond earlier 1.5 MW designs like the AR1500. This turbine’s rating reflects industry efforts to push the limits of tidal turbine capacity and contributes to broader device scaling in tidal power markets.

4) AR1500 (MeyGen) – 1.5 MW seabed tidal turbine, Scotland, UK

The AR1500 is a 1.5-MW tidal stream turbine deployed at Scotland’s MeyGen project in the Inner Sound of the Pentland Firth, which is one of the most extensively studied tidal stream projects globally. These are 1.5-MW rated turbines with 18-meter rotor diameters, installed on seabed foundations in high-velocity tidal channels. 

The design uses pitch control to maintain output above a rated flow speed and a yaw module to reorient between ebb and flood tides. In practice, AR1500-class machines helped establish multi-turbine, grid-export tidal generation at utility scale, making them a benchmark for modern tidal deployments.

5) Minesto Dragon 12 – 1.2 MW tidal “kite” turbine, Faroe Islands

Minesto’s Dragon 12 is a utility-scale tidal “kite” rated at 1.2 MW and designed to generate energy by flying on a controlled trajectory underwater. Instead of relying on a fixed seabed tower, the system harvests energy by flying a controlled underwater trajectory.

Minesto reports the system was commissioned in February 2024 at the Vestmanna site in the Faroe Islands and delivered its first electricity to the national grid on February 9, 2024. The company describes Dragon 12 as a 12-meter-wide, 28-ton subsea kite tethered to the seabed, operated through an “8-shaped” flight path to increase effective flow speed across its turbine.

6) SeaGen – 1.2 MW pioneering commercial tidal turbine, Northern Ireland, UK

SeaGen was one of the most important early commercial-scale tidal turbines, installed in Strangford Lough and rated at 1.2 MW using two 600-kW turbines on a pile-mounted structure. It was commissioned in 2008 and decommissioned in 2019, with industry reports confirming its 1.2-MW capacity. 

Project documentation notes a total investment of around £12 million, reflecting the cost of proving a large tidal device in a real marine environment. While smaller than today’s <a href="https://interestingengineering.com/lists/2…

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🔗 Sumber: interestingengineering.com