Release Notes#
Click each tab to learn about the new and updated features in each release of Intel® Embodied Intelligence SDK.
Embodied Intelligence SDK v25.14 provides necessary software framework, libraries, tools, BKC, tutorials and example codes to facilitate embodied intelligence solution development on Intel® Core Ultra Series 2 processors (Arrow Lake-H), It provides Intel’s Linux LTS kernel v6.12.8 with Preempt-RT, and supports for Canonical Ubuntu 22.04, introduces initial support for ROS2 Humble. It supports many models optimization with OpenVINO™, and provides typical workflows and examples including ACT manipulation, ORB-SLAM3, etc.
New Features:
Provided Linux OS 6.12.8 BSP with Preempt-RT
Provided Real-time optimization BKC
Optimized IgH EtherCAT master with Linux kernel v6.12
Added ACT manipulation pipeline with OpenVINO™/Intel® Extension for PyTorch optimization
Added ORB-SLAM3 pipeline focuses on real-time simultaneous localization and mapping
Provided typical AI models optimization tutorials with OpenVINO™
The following model algorithms were optimized by Intel® OpenVINO™:
Algorithm |
Description |
|---|---|
CNN based object detection |
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CNN based object detection |
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CNN based object detection |
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Transformer based segmentation |
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Extend SAM to video segmentation and object tracking with cross attention to memory |
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Lightweight substitute to SAM |
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Lightweight substitute to SAM (Same model architecture with SAM. Can refer to OpenVINO SAM tutorials for model export and application) |
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CNN based segmentation and diffusion model |
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Transformer based object detection |
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Transformer based object detection |
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Transformer based image classification |
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An end-to-end imitation learning model designed for fine manipulation tasks in robotics |
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A self-supervised framework for interest point detection and description in images, suitable for a large number of multiple-view geometry problems in computer vision |
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A model designed for efficient and accurate feature matching in computer vision tasks |
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Obtaining a BEV perception is to gain a comprehensive understanding of the spatial layout and relationships between objects in a scene |
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A powerful tool that leverages deep learning to infer 3D information from 2D images |
The following pipelines were added:
Pipeline Name |
Description |
|---|---|
Imitation learning pipeline using Action Chunking with Transformers(ACT) algorithm to train and evaluate in simulator or real robot environment with Intel optimization |
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One of popular real-time feature-based SLAM libraries able to perform Visual, Visual-Inertial and Multi-Map SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models |