Physical AI & Embodied Intelligence

What is Physical AI?

AI systems that perceive, reason about, and act in the physical world

Embodied intelligence: robots that can see, understand, plan, and manipulate

Key differences from digital AI: real-time constraints, safety-critical, edge deployment

How Python, SQL, Predictive AI, Generative AI, and Agentic AI power Physical AI

The Physical AI Stack

Perception: Cameras, LiDAR, depth sensors, IMUs (Computer Vision from Level 3)

Reasoning: Spatial understanding, task planning, world models (Generative AI from Level 4)

Action: Motors, actuators, grippers, locomotion systems

Control: Real-time feedback loops, safety systems (Python from Level 1)

Real-World Applications

Manufacturing automation, warehouse robots (AMRs)

Healthcare assistance, surgical robotics

Service robots (delivery, cleaning, hospitality)

Autonomous vehicles and drones

Humanoid robots (Tesla Optimus, Boston Dynamics Atlas)

Industry Context

NVIDIA's Physical AI ecosystem: Jetson, Isaac, Omniverse

Google Gemini Robotics: Embodied reasoning and VLA models

Boston Dynamics: Commercial robotics deployment

NVIDIA Jetson Hardware Options

Jetson Orin Nano Super: 67 TOPS, 8GB, 7-15W (~$249) - Learning, prototypes

Jetson Orin NX: 100-157 TOPS, 8-16GB, 10-25W (~$599) - AMRs, drones

Jetson AGX Orin: 200-275 TOPS, 32-64GB, 15-60W (~$1,999) - Complex robotics

Jetson Thor: 2070 FP4 TFLOPS, 128GB, 130W - Humanoids, heavy AI

JetPack SDK & Development Environment

Ubuntu 22.04-based Linux OS

CUDA for GPU acceleration (Python integration from Level 1)

TensorRT for optimized AI inference

Isaac ROS integration

Docker container support

OTA updates for fleet management

Hands-on Labs

Set up Jetson Orin Nano Super developer kit

Deploy YOLOv8 object detection with TensorRT (Deep Learning from Level 3)

Build real-time vision pipeline with multiple cameras

Optimize AI models for edge deployment (Python scripting from Level 1)

Store sensor data in database for analytics (SQL from Level 2)

Edge AI Model Deployment

Model training → Quantization (FP32→FP16→INT8→INT4)

TensorRT optimization for 5-10x speedup

Popular models: YOLOv8, SAM (Segment Anything), DepthAnything

Deploying models trained in Predictive AI (Level 3) on Jetson hardware

Isaac ROS Packages

isaac_ros_dnn_inference: Deep learning inference with GPU acceleration

isaac_ros_object_detection: Real-time object detection

isaac_ros_stereo: Depth from stereo cameras

isaac_ros_visual_slam: Real-time SLAM

NITROS: Zero-copy GPU acceleration for ROS 2

3D Perception

Depth estimation from RGB-D cameras

Point cloud processing with Python libraries (NumPy, Open3D)

3D object detection and pose estimation

Occupancy mapping for navigation

Data Pipeline & Analytics

Store perception data in SQL databases (Level 2)

Analyze detection performance metrics with Python (Level 1)

Build data pipelines for continuous model improvement

Hands-on Labs

Implement real-time object detection (30+ FPS)

Build visual SLAM system with Isaac ROS

Create 3D scene understanding pipeline

ROS 2 Fundamentals

Nodes, topics, services, actions

DDS middleware for real-time communication

Quality of Service (QoS) policies for sensor data

Python ROS 2 programming (building on Level 1)

Launch files and parameter management

Navigation Stack (Nav2)

SLAM for mapping (building on Computer Vision from Module 3)

Path planning algorithms (A*, Dijkstra, TEB)

Obstacle avoidance and dynamic replanning

Waypoint navigation and GPS integration

Behavior trees for complex navigation tasks

MoveIt 2 for Manipulation

Motion planning for robot arms

Inverse kinematics and collision checking

Pick-and-place operations

Grasp planning with perception

Multi-Robot Systems

Fleet coordination (building on Agentic AI from Level 5)

Task allocation using agent-based approaches

Centralized vs. decentralized control

Data Logging & Analytics

ROS 2 bag files for data recording

Store robot telemetry in SQL databases (Level 2)

Analyze performance with Python data science tools (pandas, matplotlib)

Hands-on Labs

Build autonomous navigation system with Nav2

Implement robot arm control with MoveIt 2

Create multi-robot coordination demo

Isaac Sim Overview

Reference application for designing, simulating, testing, and training AI-based robots

Built on NVIDIA Omniverse platform

OpenUSD: Universal scene description for 3D environments

PhysX: Realistic physics simulation

RTX: Ray-traced rendering for photorealistic visuals

Multi-GPU: Scalable simulation for large environments

Key Capabilities

Sensor simulation (cameras, LiDAR, IMU, depth sensors)

Robot dynamics and physics (kinematics, collision detection)

Synthetic data generation for training AI models (Generative AI from Level 4)

Domain randomization for sim-to-real transfer

Integration with ROS 2 (from Module 4)

Isaac Lab

Reinforcement learning framework for robot training

Policy training with multi-GPU acceleration

Sim-to-real transfer for deploying on Jetson (Module 2)

Pre-trained models and benchmarks

Data Pipeline

Generate synthetic training datasets (Python scripts from Level 1)

Store simulation results in SQL databases (Level 2)

Analyze simulation metrics for model improvement

Hands-on Labs

Set up Isaac Sim environment

Simulate robot with sensors (LiDAR, cameras)

Generate synthetic training data for vision models

Train RL policy and deploy on real Jetson hardware

Gemini Robotics Architecture

Two-Model System for Intelligent Robotics:

Gemini Robotics-ER (Embodied Reasoning): High-level brain for planning & logical decisions

• Spatial understanding & 2D/3D pointing
• Task planning & decomposition (building on Agentic AI from Level 5)
• Tool calling (Google Search, APIs)
• Progress monitoring

Gemini Robotics (Vision-Language-Action): Direct motor control

• Fine manipulation (origami folding, salad prep)
• Cross-embodiment transfer
• Dexterous movements

Key Capabilities & Use Cases

Spatial Reasoning: "Point at all objects you can pick up" / "What's the weight of this box?"

Task Planning: "Clean up the table and organize by category"

Context-Aware: "Sort trash into correct bins based on my location" (uses Google Search)

Thinking Budget: Small (fast spatial tasks) vs. Large (complex reasoning)

Integration Pattern

User Command → Gemini Robotics-ER (Plans & Reasons) → Task Breakdown + Tool Calls → Robot Controller API / Gemini VLA → Physical Actions

Python Implementation

Set up Gemini API in Google AI Studio (Python from Level 1)

Implement spatial reasoning for object detection

Build task planning system with tool calling

Integration with ROS 2 for robot control (Module 4)

Data & Analytics

Log agent decisions in SQL databases (Level 2)

Analyze task success rates with Python analytics

A/B testing different prompts and thinking budgets

Hands-on Labs

Implement spatial reasoning for object manipulation

Build task planning system with Google Search integration

Create end-to-end agentic behavior (e.g., "prepare coffee")

Deploy on Jetson hardware with real robot

NVIDIA Isaac COMPASS

Vision-based mobility foundation model

Cross-embodiment navigation across different robot types

Terrain understanding and obstacle detection

Deployment on Jetson for real-world autonomy

NVIDIA GR00T for Humanoid Robotics

NVIDIA's humanoid foundation model

Whole-body control and bipedal locomotion

Human-robot interaction capabilities

Industry applications: Tesla Optimus, Figure AI

Multi-Robot Systems & Fleet Management

Fleet management architectures (building on Agentic AI from Level 5)

Task allocation algorithms for robot teams

Coordinated manipulation with multiple robots

Fleet monitoring dashboards with SQL analytics (Level 2)

Production Deployment

Model containerization with Docker

Over-the-Air (OTA) updates for robot fleets

Safety mechanisms (emergency stops, watchdogs)

Performance optimization & latency reduction

Cloud-edge hybrid architectures

Industry Standards & Safety

ISO 10218 (Industrial Robot Safety)

ISO/TS 15066 (Collaborative Robot Safety)

Risk assessment and hazard analysis

Cybersecurity for robotic systems

Project Options (Choose One):

Option 1: Warehouse Assistant Robot

• Perception: Object detection, barcode scanning (Module 3)
• Planning: Autonomous navigation (Module 4)
• Action: Pick-and-place with manipulation
• Hardware: Jetson Orin NX + Mobile base + Arm
• Integration: All 6 AI levels (Python, SQL, Predictive, Generative, Agentic, Physical)

Option 2: Table-Top Manipulation Assistant

• Perception: Scene understanding with RGB-D cameras (Module 3)
• Planning: Grasp planning with Gemini ER (Module 6)
• Action: Precise manipulation with robot arm
• Hardware: Jetson Orin Nano + Robot arm
• Integration: Python control, SQL logging, Predictive maintenance

Option 3: Interactive Service Robot

• Perception: Human detection, gesture recognition
• Planning: Task understanding with Gemini ER (Module 6)
• Action: Object delivery and navigation
• Hardware: Jetson AGX Orin + Mobile platform
• Integration: Agentic AI for autonomous decision-making

Deliverables:

System architecture document

Implementation code (Python, ROS 2, Isaac ROS)

Testing report (simulation in Isaac Sim + real-world deployment)

SQL database with performance analytics

Demo video showcasing all 6 AI levels in action

Technical presentation (15 minutes)

Humanoid Robot Design Principles

Bipedal Locomotion & Balance Control

Natural Language Processing for Human-Robot Interaction

Emotion Recognition & Social Robotics

Industry Example: Tesla Optimus, Figure AI

Surgical Robotics & AI-Assisted Surgery

Rehabilitation Robotics

Elderly Care & Companion Robots

Medical Diagnostic Systems

Industry Example: Da Vinci Surgical System, Intuitive Surgical

Precision Agriculture with Drones

Autonomous Harvesting Systems

Environmental Monitoring Robots

Rugged Terrain Navigation

Industry Example: John Deere Autonomous Tractors, FarmWise