📌 TOPINDIATOURS Update ai: ByteDance Introduces Astra: A Dual-Model Architecture f

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

📌 TOPINDIATOURS Eksklusif ai: Marble enters the race to bring AI to tax work, arme

Marble, a startup building artificial intelligence agents for tax professionals, has raised $9 million in seed funding as the accounting industry grapples with a deepening labor shortage and mounting regulatory complexity.

The round, led by Susa Ventures with participation from MXV Capital and Konrad Capital, positions Marble to compete in a market where AI adoption has lagged significantly behind other knowledge industries like law and software development.

"When we looked at the economy and asked ourselves where AI is going to transform the way businesses operate, we focused on knowledge industries — specifically businesses with hourly fee-based service models," said Bhavin Shah, Marble's chief executive officer, in an exclusive interview with VentureBeat. "Accounting generates $250 billion in fee-based billing in the US every year. There's a tremendous opportunity to increase efficiency and improve margins for accounting firms."

The company has launched a free AI-powered tax research tool on its website that converts complex government tax data into accessible, citation-backed answers for practitioners. Marble plans to expand into AI agents that can analyze compliance scenarios and eventually automate portions of tax preparation workflows.

Marble's backers share Shah's conviction about the market. "Marble is rethinking the accounting system from the ground up. Accounting is one of the biggest — and most overlooked — markets in professional services," Chad Byers, general partner at Susa Ventures, told VentureBeat. "We've known Bhavin from his time as an executive in the Susa portfolio, and have seen firsthand how sharp and execution-driven he is. He and Geordie bring the perfect mix of operational depth and product instinct to a space long overdue for change — and they see the same massive opportunity we do."

The accounting industry lost 340,000 workers in four years — and replacements aren't coming

Marble enters a market shaped by structural forces that have fundamentally altered the economics of professional accounting.

The accounting profession has shed roughly 340,000 workers since 2019, a 17% decline that has left firms scrambling to meet client demands. First-time candidates for the Certified Public Accountant exam dropped 33% between 2016 and 2021, according to AICPA data, and 2022 saw the lowest number of exam takers in 17 years.

The exodus comes as baby boomers exit en masse. The American Institute of CPAs estimates that approximately 75% of all licensed CPAs reached retirement age by 2019, creating a demographic cliff that the profession has struggled to address.

“Fewer CPAs are getting certified year over year," Shah said. "The industry is compressing at the same time that there's more work to be done and the tax code is getting more complicated."

The National Pipeline Advisory Group, a multi-stakeholder body formed by the AICPA in July 2023, released a report identifying the 150-hour education requirement for CPA licensure as a significant barrier to entry. A separate survey by the Center for Audit Quality found that 57% of business majors who chose not to pursue accounting cited the additional credit hours as a deterrent.

Recent legislative changes reflect the urgency. Ohio now offers alternatives to the 150-hour requirement, signaling that states are willing to experiment with pathways that could reverse enrollment declines.

Why AI transformed law and software development but left accounting behind

Despite the profession's challenges, AI adoption in accounting has moved more slowly than in adjacent knowledge industries. Harvey and Legora have raised hundreds of millions to bring AI to legal work. Cursor and other coding assistants have transformed software development. Accounting, by contrast, remains largely dependent on legacy research platforms and manual processes.

Geordie Konrad, Marble's executive chairman and a co-founder of restaurant software company TouchBistro, attributes the gap to how people conceptualize AI's capabilities.

“It was obvious to many people that LLMs could do meaningful work by manipulating code for software developers and manipulating words for lawyers. In the accounting industry, LLMs are going to be used as reasoning agents," Konrad said. " That requires a bit more of a two-step analysis to see why it's a big opportunity."

The technical challenge is substantial. Tax regulations form one of the most complex, interconnected information systems that humans have created — tens of thousands of interlocking rules, guidance documents, and jurisdiction-specific requirements that frequently overlap or conflict.

"If you want to put AI through its paces and ask how far it's come in replicating cognitive functions, this is an unbelievable playground to work in," Konrad said.

A dramatic shift: AI adoption among tax and finance teams doubles in one year

Recent data suggests the accounting profession's stance toward AI is shifting rapidly.

A 2025 survey from Hanover Research and Avalara found that 84% of finance and tax teams now use AI heavily in their operations, up from 47% in 2024. The 2025 Generative AI in Professional Services Report from Thomson Reuters Institute found that 21% of tax firms already use generative AI technology, with 53% either planning to adopt it or actively considering it.

Large accounting firms have invested heavily in AI infrastructure. Deloitte has developed generative AI capabilities within its audit platform. BDO announced a $1B investment in AI over the next five years. EY launched an AI platform combining technology with strategy, transactions, and tax services. PwC estimates a complete A…

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🔗 Sumber: venturebeat.com