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

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

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: Baseten takes on hyperscalers with new AI training platfo

Baseten, the AI infrastructure company recently valued at $2.15 billion, is making its most significant product pivot yet: a full-scale push into model training that could reshape how enterprises wean themselves off dependence on OpenAI and other closed-source AI providers.

The San Francisco-based company announced Thursday the general availability of Baseten Training, an infrastructure platform designed to help companies fine-tune open-source AI models without the operational headaches of managing GPU clusters, multi-node orchestration, or cloud capacity planning. The move is a calculated expansion beyond Baseten's core inference business, driven by what CTO Amir Haghighat describes as relentless customer demand and a strategic imperative to capture the full lifecycle of AI deployment.

"We had a captive audience of customers who kept coming to us saying, 'Hey, I hate this problem,'" Haghighat said in an interview. "One of them told me, 'Look, I bought a bunch of H100s from a cloud provider. I have to SSH in on Friday, run my fine-tuning job, then check on Monday to see if it worked. Sometimes I realize it just hasn't been working all along.'"

The launch comes at a critical inflection point in enterprise AI adoption. As open-source models from Meta, Alibaba, and others increasingly rival proprietary systems in performance, companies face mounting pressure to reduce their reliance on expensive API calls to services like OpenAI's GPT-5 or Anthropic's Claude. But the path from off-the-shelf open-source model to production-ready custom AI remains treacherous, requiring specialized expertise in machine learning operations, infrastructure management, and performance optimization.

Baseten's answer: provide the infrastructure rails while letting companies retain full control over their training code, data, and model weights. It's a deliberately low-level approach born from hard-won lessons.

How a failed product taught Baseten what AI training infrastructure really needs

This isn't Baseten's first foray into training. The company's previous attempt, a product called Blueprints launched roughly two and a half years ago, failed spectacularly — a failure Haghighat now embraces as instructive.

"We had created the abstraction layer a little too high," he explained. "We were trying to create a magical experience, where as a user, you come in and programmatically choose a base model, choose your data and some hyperparameters, and magically out comes a model."

The problem? Users didn't have the intuition to make the right choices about base models, data quality, or hyperparameters. When their models underperformed, they blamed the product. Baseten found itself in the consulting business rather than the infrastructure business, helping customers debug everything from dataset deduplication to model selection.

"We became consultants," Haghighat said. "And that's not what we had set out to do."

Baseten killed Blueprints and refocused entirely on inference, vowing to "earn the right" to expand again. That moment arrived earlier this year, driven by two market realities: the vast majority of Baseten's inference revenue comes from custom models that customers train elsewhere, and competing training platforms were using restrictive terms of service to lock customers into their inference products.

"Multiple companies who were building fine-tuning products had in their terms of service that you as a customer cannot take the weights of the fine-tuned model with you somewhere else," Haghighat said. "I understand why from their perspective — I still don't think there is a big company to be made purely on just training or fine-tuning. The sticky part is in inference, the valuable part where value is unlocked is in inference, and ultimately the revenue is in inference."

Baseten took the opposite approach: customers own their weights and can download them at will. The bet is that superior inference performance will keep them on the platform anyway.

Multi-cloud GPU orchestration and sub-minute scheduling set Baseten apart from hyperscalers

The new Baseten Training product operates at what Haghighat calls "the infrastructure layer" — lower-level than the failed Blueprints experiment, but with opinionated tooling around reliability, observability, and integration with Baseten's inference stack.

Key technical capabilities include multi-node training support across clusters of NVIDIA H100 or B200 GPUs, automated checkpointing to protect against node failures, sub-minute job scheduling, and integration with Baseten's proprietary Multi-Cloud Management (MCM) system. That last piece is critical: MCM allows Baseten to dynamically provision GPU capacity across multiple cloud providers and regions, passing cost savings to customers while avoiding the capacity constraints and multi-year contracts typical of hyperscaler deals.

"With hyperscalers, you don't get to say, 'Hey, give me three or four B200 nodes while my job is running, and then take it back from me and don't charge me for it,'" Haghighat said. "They say, 'No, you need to sign a three-year contract.' We don't do that."

Baseten's approach mirrors broader trends in cloud infrastructure, where abstraction layers increasingly allow workloads to move fluidly across providers. When AWS experienced a major outage several weeks ago, Baseten's inference services remained operational by automatically routing traffic to other cloud providers — a capability now extended to training workloads.

The technical differentiation extends to Baseten's observability tooling, which provides per-GPU metrics for multi-node jobs, granular checkpoint tracking, and a refreshed UI that surfaces infrastructure-level events. The company also introduced an "ML Cookbook" of open-source training recipes for popular models like Gemma, GPT OSS, and Qwen, designed to help users reach "training success" faster.

Early adopters report 84% cost savings and 50% latency improvements with custom models

Two early customers illustrate the market Baseten is targeting: AI-native companies building specialized vertical solutions that require custom models.

Oxen AI, a platform focused on dataset management and model fine-tuning, exemplifies the partnership model Baseten env…

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🔗 Sumber: venturebeat.com