Robotics AI Suite#
Edge AI Suites are collections of open, industry-specific AI software development kits (SDKs), microservices, and sample applications for independent software vendors (ISVs), system integrators and solution builders.
The Robotics AI Suite is an open-source toolkit for developing robots that sense, interact, and make decisions at the edge. Built on a unified Intel platform, the Suite combines modular tools for vision, control, and AI inference, accelerating integration and deployment.
Whatever your robotics workload - if you are bringing up a new platform, integrating sensors and actuators, or optimizing an AI workload with Intel - these documents help you find compatible ingredients and pipelines for your robotics application and guidance for deploying on Intel hardware.
Industry Segments#
The Robotics AI Suite targets the following industry segments:
Autonomous Mobile Robot
— Wheeled or tracked robots that navigate dynamic environments without fixed guidance, using onboard sensing, mapping, and path planning. Common in warehouse logistics, material transport, inspection, and last-mile delivery.Humanoid
— Human-shaped robots with articulated limbs designed to operate in spaces and with tools built for people. Used for manipulation, locomotion, and interactive tasks in service, research, and general-purpose automation.Stationary Arm
— Fixed-base robotic manipulators that perform precise, repeatable operations within a defined workspace. Typical applications include pick-and-place, assembly, welding, and machine tending on production lines.
Reference Applications and Ingredients#
The table below lists the reference applications, sample pipelines, and tutorials available across this documentation. The Domains column categorizes each entry to help you find relevant material for your application.
Reference |
Domains |
Description |
|---|---|---|
Sensors, Middleware |
Integrates an RealSense camera with ROS 2 to stream color and depth data, launch camera nodes, and visualize the feed in RViz2. |
|
3D Pointcloud Groundfloor Segmentation for RealSense Camera and 3D LiDAR |
Sensors, AI |
Intel algorithm that classifies 3D point clouds from RealSense or LiDAR sensors into ground, elevated surfaces, and obstacles for navigation over challenging terrain. |
AI, OpenVINO, Sensors |
Runs OpenVINO™-optimized YOLOv8 object detection and segmentation in parallel across up to four USB or GMSL cameras. |
|
AI, OpenVINO, Sensors, Middleware |
Deploys a ROS 2 OpenVINO™ node for object detection with selectable CPU, GPU, or NPU inference devices. |
|
AI, OpenVINO, Sensors, Middleware |
Installs a ROS 2 OpenVINO™ node and runs a YOLOv8 segmentation model on the CPU using a RealSense camera image as input. |
|
AI, OpenVINO, Sensors, Middleware |
Runs a ROS 2 OpenVINO™ semantic segmentation model on CPU or GPU using a RealSense camera image as input. |
|
Autonomous Mobile Robot, SLAM |
Multi-robot visual SLAM optimized with SSE/AVX2 instruction sets for map building and merging on Intel® CPUs and GPUs. |
|
Autonomous Mobile Robot, Sensors |
Intel-optimized octomap implementation that builds 3D voxel maps from RealSense depth camera data for efficient environment representation. |
|
Autonomous Mobile Robot, AI, Sensors |
Adaptive DBSCAN person detection and tracking from 2D/3D LiDAR or RealSense point clouds, with Gazebo simulation and real-robot deployment examples. |
|
Autonomous Mobile Robot, Navigation |
Intel patented global path planner delivering 20-30x speedup over A* for the ROS 2 Navigation2 stack. |
|
Autonomous Mobile Robot, Navigation |
Re-localization algorithm that rapidly recovers robot pose in Nav2 after sensor glitches or environment disturbances. |
|
Autonomous Mobile Robot, SLAM |
GPU-accelerated keypoint and descriptor extraction for Visual SLAM front-ends, with OpenCV and OpenCV-free APIs. |
|
Autonomous Mobile Robot |
Validates motor control on a deployed robot using keyboard teleoperation before running autonomous workloads. |
|
Autonomous Mobile Robot, Navigation |
Deploys the Wandering autonomous exploration pipeline on a physical robot using RTAB-Map and Nav2. |
|
Autonomous Mobile Robot, Simulation |
Introduces simulating robots as digital twins in Gazebo to test robotics applications before real-world deployment. |
|
Autonomous Mobile Robot, Simulation, Navigation |
Simulates the full Wandering pipeline in Gazebo with mapping, frontier exploration, and Nav2-based navigation. |
|
Middleware, Manipulation, Simulation |
Coordinates two UR5 arms and a TurtleBot3 AMR on a conveyor line using MoveIt2 and Nav2 in Gazebo Classic. |
|
Humanoid, AI, OpenVINO, Manipulation |
Imitation learning pipeline using Action Chunking with Transformers, optimized with OpenVINO™, for fine manipulation in simulation and on real ALOHA robots. |
|
Humanoid, AI, Manipulation |
Combines ACT imitation learning with OCS2 model predictive control and MuJoCo simulation for perception-action manipulation control. |
|
Humanoid, AI, OpenVINO, Manipulation |
Visuomotor diffusion-policy pipeline for the Push-T manipulation task, with Transformer- and CNN-based variants optimized by OpenVINO™. |
|
Humanoid, SLAM |
Real-time feature-based Visual SLAM supporting monocular, stereo, and RGB-D cameras, with EUROC dataset and RealSense demos. |
|
Humanoid, AI, Manipulation |
Code-generation pipeline combining an LLM (Phi-4), vision models (SAM, CLIP), and a JAKA arm for voice- or text-commanded robot control. |
|
Humanoid, AI, OpenVINO, Manipulation |
Bimanual manipulation foundation model with a unified action space, running in MuJoCo simulation and on real ALOHA robots with OpenVINO™ optimization. |
|
Humanoid, AI, OpenVINO, Manipulation |
Vision-Language-Action pipeline pairing a PaliGemma VLM with a flow-matching policy and real-time chunking for smooth high-frequency control, accelerated with OpenVINO™. |
|
AI, OpenVINO, Manipulation |
Imitation learning model that predicts action chunks with Transformers for fine manipulation, including conversion to OpenVINO™ IR. |
|
AI, OpenVINO, Manipulation |
Visuomotor policy using conditional denoising diffusion to handle multimodal action distributions, with low-dim and image variants and OpenVINO™ conversion. |
|
AI, OpenVINO, Manipulation |
1.2B-parameter diffusion foundation model for manipulation pre-trained on 46 datasets, with OpenVINO™ IR conversion guidance. |
|
AI, OpenVINO, Manipulation |
Vision-Language-Action foundation model pairing a PaliGemma VLM with an action-expert diffusion transformer, including OpenVINO™ conversion. |
|
AI, OpenVINO, Manipulation |
Behavior cloning models using RNN or Transformer backbones to map observations to actions from expert demonstrations. |
|
AI, OpenVINO, Manipulation |
Graph neural network image-based visual servo policy achieving sub-millimeter precision at real-time (~40 fps) rates. |
|
AI, OpenVINO, Manipulation |
Grasp generation model trained on GraspNet-1Billion that predicts scored 6-DoF grasp poses from point clouds. |
|
AI, OpenVINO, SLAM |
Self-supervised interest point detector and descriptor generator with homographic adaptation for cross-domain generalization. |
|
AI, OpenVINO, SLAM |
Lightweight transformer feature matcher with adaptive depth and width for efficient correspondence in 3D reconstruction and localization. |
|
AI, OpenVINO, Manipulation |
Enhanced 3D manipulation policy that encodes point clouds with a 3D visual encoder and generates actions via diffusion. |
|
AI, OpenVINO, Navigation |
Efficient multi-scale bird’s-eye-view perception model for obstacle avoidance, path planning, and spatial awareness. |
|
AI, OpenVINO, Sensors |
Monocular depth estimation foundation model (25M-1.3B parameters) for cost-effective depth perception without LiDAR. |
|
Benchmarking, Autonomous Mobile Robot |
Automated benchmarking of the Wandering AMR pipeline, measuring latency, resource usage, and optional GPU/NPU KPIs across runs. |
|
Benchmarking, Manipulation |
Automated benchmarking of the multi-robot Pick & Place simulation, capturing lifecycle metrics and aggregated KPIs. |
Explore Intel Robotics Ecosystem#
Scale Enablement - Robotics Builders Community | Intel(R) Industry Solution Builders
Explore robotics Scale Enablement and discover how Intel’s ecosystem and experts help accelerate robotics solutions from design to deployment.
Edge AI Partner Spotlight - Solution Hub | Intel(R) Industry Solution Builders
Browse our curated catalog of Intel-powered Edge AI systems and applications, delivering real-time innovation, efficiency, and intelligence to your business.
Next Steps#
Continue to the System Requirements guide to learn about supported Intel processors, OS requirements, and development kits:
System Requirements — select a platform and install an OS distribution.
