Overview
Leveraging Intel's long-standing industrial robotics expertise, the Robotics AI Suite provides tools, pipelines, reference applications, pipelines, and models optimized for real-time physical AI performance and scalability.
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Robotics AI Suite launch
Everywhere
Intel adds robotics to its AI Suites to enable robot builders to develop and scale robotic systems faster, with curated Vision AI, Gen AI, acceleration libraries and tools to enable AMRs, stationary, and humanoid robots.
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Arizona, USA
Intel announces the Robotics AI Suite at a press event showcasing the art-of-the-possible for agentic and physical AI use cases demonstrating Panther Lake performance.
Learn MoreTechnology & Performance
Leverage Intel’s long-standing industrial robotics expertise, advanced compute to power real-time controls and AI acceleration on a single processor, and an open ecosystem to adapt next-generation robotics and scale physical AI-based robotics faster.
Advanced Compute
Intel delivers integrated, optimized hardware performance across a range of silicon form factors and systems to power AI acceleration with low-latency and scalability at the edge.
Intel® Core™ Ultra Processors
Power both real-time performance and AI acceleration on Intel® Core™ Ultra Series 2 and Series 3 processors to deliver efficiency and cost savings.
GPUs for AI Acceleration
Stream media analytics and deep learning pipelines at the edge with powerful GPU to accelerate advanced physical AI and fast model inference at the edge.
AI Systems Extend x86 Performance
Extend AI for robot systems with pre-built AI systems designed with best known hardware configurations and flexible form factors for delivering physical AI at the edge.
Build with an open ecosystem
Intel integrates across a broad ecosystem of hardware partners to enable freedom of choice and flexibility for building robots with best-known hardware configurations for cameras, sensors, and hardware connectivity to enable rugged, long-lasting performance.
Camera and Sensor Integration
With support for high-speed image data transfer technologies like MIPI CSI-2, robot builders have high bandwidth to capture high-resolution video and images with integrations for RealSense® cameras.
Controllers and actuators
Intel’s robotics platform supports many types of actuators including motors and servo-drives to control heavy duty and light weight robotic arms, wheeled platforms, and end effectors for gripping, picking, and placing.
Fast Connectivity
Leveraging industry-standard industrial automation protocols for high speed and low latency, like EtherCAT and CANopen, Intel’s hardware configuration supports high precision, high-speed connectivity and device interoperability for cost-effective device integration.
Robotics Control Recipes
Extend real time performance with repeatable efficiencies for predictable, scalable, and adaptable performance and real time control across rugged industrial deployments at the edge.
Intel Time Sensitive Networking
Optimized network and ethernet contols streamline robot operations for efficiency and low latency.
Intel Time Coordinated Computing (TCC)
A processor technology that prioritizes workloads to optimize performance for time-sensitive, real-time applications to reduce jitter, improve determinism, and synchronize tasks with microsecond-accuracy.
Virtualization and Application Management
Right-size robotics solutions to ensure scalability, compatibility, and performance tuned for physical AI workloads, and suitable to scale industrial robotics operations.
Real-time controls, manageability and deployment
Enable containerized and virtualized applications in edge deployments to process AI pipelines with speed and efficiency on CPU, NPU, and iGPU.
Intel® ACRN
A lightweight hypervisor used in autonomous mobile robots (AMRs) to consolidate multiple operating systems and workloads onto a single computing platform. It uses virtualization to securely and reliably run a mix of critical real-time tasks (like navigation) and general-purpose workloads (like AI) on the same hardware.
Kernel-based Virtual Machine
Intel brings special features for Kernel-based Virtual Machine to consolidate compute-intensive tasks to ensure real-time performance and robust remote management for edge devices and reduce latency.
Evaluation, qualification, and benchmarking
Deploy and scale robotics applications with near zero-touch deployment and enable remote management to quickly evaluate AI workloads using performance monitoring, repeatable benchmarks, and optimization tools to ensure efficiency and optimized performance.
Visual Pipeline and Performance Evaluation Tool (ViPPET)
Evaluate hardware for AI workloads to benchmark CPU frequency, GPU power usage, memory use, and temperature with hardware-aware tuning to fine-tune performance.
Device Qualification and Benchmark Tool
Verify acceleration and compute to qualify “best known” AI+ media pipeline configurations to confirm reliability, up-time, power, and memory efficiency.
Validation Framework
Observe best case scenarios using telemetry data to compare OpenVINO™ benchmarks, media performance, video pipeline, Memory, GPU AI frequencies. Use the Robotics AI Suite reference applications and OTX validation framework to evaluate trained models on dedicated validation data sets to assess model quality and capabilities to achieve desired performance outcomes.
Use Cases
Accelerate your edge Al application development journey with demos and reference applications for common robotics tasks.
Facilitate robot chatbot interaction using LLMs, Vision AI, and a user’s voice to prompt robot agentic AI with text commands that generate actions.
Action Chunking with Transformers
An end-to-end imitation learning model designed for fine robotics manipulation tasks, learned from human actions and demos to avoid errors and predict actions in chunks. Reduces training time while increasing success rates.
LLM Task Planning Demo
Facilitate robot chatbot interaction using LLMs, Vision AI, and a user’s voice to prompt robot agentic AI with text commands that generate actions.
Robotics Diffusion Transformer
Pipeline enables bi-manual manipulation by processing multimodal inputs to generate robot action sequences with time series analytics and VLA models to teach physical environment and simulation tasks.
VSLAM:ORB-SLAM3
Real-time, feature-based SLAM library for visual mapping to capture multi-camera and sensor inputs to sense surrounding environment. Maximizes reliability and accuracy for poses and predictions.
SLAM and ROS2 move AMRs safely around environments with a wandering reference application optimized for real-time control and AI acceleration.
GroundFloor Segmentation
A demo segmenting depth sensor data using LiDAR and RealSense camera inputs to render a 3D pointcloud to address non-flat floors, ramps, inclines and other challenging conditions.
ITS-Planner
ROS 2 plug-in to enable global path planning based on intelligent sampling and 2-way search to reduce compute time to reach robot about its position in a grid map and where it’s headed.
Simulation
Tutorials that show how to use the ROS 2 simulations with Intel® Robotics AI Dev Kit. Robot sensing and navigation can be tested in these simulated environments.
Wandering
A ROS 2 compatible reference application to use with different SLAM libraries to move robots around in an unknown environment to create navigation map of the environment.
Tackle pick and place and track and place challenges using the Robot Vision and Control (RVC) software framework to enable new industrial robotics tasks and capabilities.
Robot Vision and Control Framework (Intel® RVC)
Robot Vision and Control is a robotic software framework aimed at tackling Pick and place, Track and place industrial problems.
Powerful transformer-based models reduce AI model’s computational and memory demands while adding context to autonomous robots with IoT sensor input.
Vision AI Models
Allow robots to perform complex manipulations like picking, placing, and assembly with Vision AI for object detection, classification, and mapping with precise, real-time control.
LLM, VLM, VLA and Gen AI Models
Enable robots to interpret and act in their environment by combining visual input with natural language understanding to generate and execute physical actions.
Diffusion and Transformer Models
Diffusion policy and transformer-based models improve robot manipulation by using Action Chunking with Transformers (ACT) to predict sequences of actions from observations and language instructions.
Benefits
Bring new robotics innovation to market faster and extend the functionality of existing robotics deployments optimized for performance and acceleration at the edge, while reducing complexity and cost.
Deliver scalable physical AI solutions to market faster
Accelerate and optimize vision AI, media analytics pipelines, and Gen AI workloads on Intel's proven, long-standing robotics expertise, without starting from scratch.
Extend and scale x86-based robotics innovation
Build with an open ecosystem on verified AI systems, benchmarked for real-world use cases to achieve repeatable performance.
Reduce complexity and improve TCO
Develop physical AI optimized with an integrated CPU, GPU and NUP to accelerate real-time controls and AI performance on one processor.
Partners
A broad, open ecosystem of robotics partners support Intel’s Robotics AI Suite and are actively integrating physical AI on Intel® silicon to enable advanced capabilities with real-time controls to power humanoids, autonomous mobile, and industrial stationary robots.
Resources
Get started using the Robotics AI Suite on GitHub or use the reference applications to build real world vision AI and Gen AI use cases on Intel silicon.
Expand robotics technical and industry perspective and learn how Intel optimizes performance and scalability of edge AI
Simplify Physical AI Development with Intel’s Robotics AI Suite
Level the playing field with physical AI at the edge and simplify the journey to develop, deploy, and scale robotics with Intel’s Robotics AI Suite.
Developers Shortcut to Smarter Robots with Intel’s Robotics AI Suite
Take the complexity out of setting up and training physical AI on Intel with the Robotics AI Suite to enhance functions for perception, locomotion, manipulation, and imitation learning.
Announcing the Open Edge Platform 2025.1 Release
Get under the hood of the latest Open Edge Platform release to learn more about the latest Geti, Scenescape, and DL Streamer updates that accelerate Vision AI, media analytics pipelines, and Gen AI.
Vision AI on Geti™ accelerated with Arc™ B-Series GPU enables robotics test automation
Learn how Intel Foundry uses Geti and Intel Iris to scale efficiency and accuracy for automated testing to reduce down time, increasing quality, and accelerate and throughput.
Extend robotics and physical AI knowledge and achieve successful outcomes with videos to get more out of the Robotics AI Suite.
Robotics at the Edge: Where AI Meets Real-World Automation
Robotics is emerging as a powerful proving ground for AI. Discover how OpenVINO accelerates AI inference and performance, power efficiency requirements that enable real-time decision-making.
Edge Insights for AMRs
Intel’s Rajesh Kurusetty explains the components available for developing Ambulatory Mobile Robots with Intel software and hardware components.
Technical papers, solution briefs, and case studies to guide efficiency and scalability from best known configurations to software optimizations, benchmarks, and use cases.
Trossen Uses Intel Technology for Powerful AI/ML Workstation
Developing AI/ML models requires an investment in extreme compute power. For models that need low latency and data privacy, the TOTL Workstation supports robotics at the edge.
Fast track time to value developing physical AI and robotics solutions on Intel® silicon with quick-start developer guides for all kinds of robots.
Autonomous Mobile Robot
Based on ROS 2, the Autonomous Mobile Robot software packages and pre-validated hardware modules feature capabilities for senser data ingestion, classification, modeling, action planning and control.
Robot Vision and Control Framework (RVC)
Centered around motion control the RVC eases pick and place operations for advanced manipulation tasks with support for identification, grasping, and relocation of items.
Humanoid-Imitation Learning
Designed to streamline the development of embodied intelligence applications on intel with comprehensive Action Chunking with Transformers demo.
Developer Tools
Accelerate development with tools and tutorials to enable robot solutions with advanced Vision AI, spatial intelligence, and optimizations and transformers for AI model inference.
Build and optimize embodied intelligence models and workflows with tools to optimize performance on Intel.
Intel® OpenVINO Training Extensions
Libraries to accelerate PyTorch, ML, and LLM models for optimized performance and fine-tuning on Intel® hardware
Geti™ Computer Vision Models
Simplify and accelerate computer vision model training with an intuitive end-to-end seamless workflow. Optimize models for speed and fine grained precision to detect objects and key points for pose estimation.
Get advice for arranging heterogeneous computing, and models used for embodied intelligence with helpful tutorials
Heterogeneous Computing
Learn about the various software workloads integrated to enable autonomous systems to perform complex tasks in dynamic edge environments.
AI Model Tutorials
Discover deep learning models commonly used in embodied intelligence solutions and how to run them on Intel platforms complete with benchmarks and Jupyter notebooks.
OpenVINO™ Toolkit
Get started with the OpenVINO™ Toolkit to simplify the development of AI inference, computer vision, automatic speech recognition, and NLP on Intel® hardware.
Support
Provides comprehensive support for Edge AI and Intel technologies, delivered by global professional technical experts.
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