ROI-Driven Foveated Attention for Unified Egocentric Representations in Vision-Language-Action Systems

2026-03-21

AI Summary

This work addresses the high cost of acquiring physical interaction data in embodied intelligence, the difficulty of cross-embodiment alignment, and the challenge of transferring internet-scale visual data to control tasks. To this end, the authors propose a region-of-interest (ROI)-driven data abstraction framework. By projecting end-effector poses via forward kinematics onto a single external camera view, the method generates hand-centered, geometrically aligned ROI representations. Integrated with ROI scaling, deterministic boundary handling, and multimodal synchronization, it forms an end-to-end reproducible processing pipeline. Notably, this approach achieves viewpoint normalization and embodiment alignment without requiring wrist-mounted cameras or multi-view systems, producing embodied representations that retain high local information density while preserving global context. This significantly enhances data reusability across heterogeneous robots, improves cross-embodiment learning efficiency, and boosts system scalability.

Technology Category

• Intelligent Robots: Embodied AI
• Computer Vision: Low Level & Physics-based Vision
• Humans and AI: Interaction Techniques and Devices

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Data transparency and provenance

Abstract

The development of embodied AI systems is increasingly constrained by the availability and structure of physical interaction data. Despite recent advances in vision-language-action (VLA) models, current pipelines suffer from high data collection cost, limited cross-embodiment alignment, and poor transfer from internet-scale visual data to robot control. We propose a region-of-interest (ROI) driven engineering workflow that introduces an egocentric, geometry-grounded data representation. By projecting end-effector poses via forward kinematics (FK) into a single external camera, we derive movement-aligned hand-centric ROIs without requiring wrist-mounted cameras or multi-view systems. Unlike directly downsampling the full frame, ROI is cropped from the original image before resizing, preserving high local information density for contact-critical regions while retaining global context. We present a reproducible pipeline covering calibration, synchronization, ROI generation, deterministic boundary handling, and metadata governance. The resulting representation is embodiment-aligned and viewpoint-normalized, enabling data reuse across heterogeneous robots. We argue that egocentric ROI serves as a practical data abstraction for scalable collection and cross-embodiment learning, bridging internet-scale perception and robot-specific control.

Problem

• embodied AI
• vision-language-action
• cross-embodiment alignment
• data efficiency
• robot control

Innovation

• ROI-driven foveated attention
• egocentric representation
• cross-embodiment alignment
• forward kinematics projection
• vision-language-action systems