Physical AI—the term NVIDIA coined at CES 2026—represents a fundamental shift in how we build intelligent systems that interact with the real world. For software developers, this opens up entirely new application categories that were previously the domain of specialized robotics engineers.

This guide will help you understand the landscape and start building with Physical AI.

Robotics Development Physical AI bridges the gap between digital intelligence and real-world interaction

What is Physical AI?

Physical AI refers to AI systems that can:

  1. Understand physical environments - 3D spatial awareness, object recognition
  2. Predict physical outcomes - How objects will move, interact, fall
  3. Plan physical actions - Navigation, manipulation, coordination
  4. Execute in the real world - Control robots, vehicles, machines

Unlike traditional AI that operates on text, images, or code, Physical AI must deal with the messiness of reality: gravity, friction, unexpected obstacles, and real-time constraints.

The Software Stack

┌─────────────────────────────────────────────────────────────┐
│                    Physical AI Stack                         │
├─────────────────────────────────────────────────────────────┤
│                                                              │
│  Application Layer                                           │
│  ├── Task Planning (What to do)                             │
│  ├── Motion Planning (How to move)                          │
│  └── Behavior Control (When to act)                         │
│                                                              │
│  Foundation Models                                           │
│  ├── NVIDIA Cosmos (World simulation)                       │
│  ├── Vision-Language Models (Scene understanding)           │
│  └── Manipulation Models (Grasping, handling)               │
│                                                              │
│  Simulation & Training                                       │
│  ├── Isaac Sim (NVIDIA)                                     │
│  ├── Gazebo (Open source)                                   │
│  └── MuJoCo (DeepMind)                                      │
│                                                              │
│  Robot Operating System                                      │
│  ├── ROS 2 (Standard framework)                             │
│  ├── Navigation Stack                                       │
│  └── MoveIt 2 (Manipulation)                                │
│                                                              │
│  Hardware Abstraction                                        │
│  ├── Motor controllers                                      │
│  ├── Sensor interfaces                                      │
│  └── Safety systems                                         │
│                                                              │
└─────────────────────────────────────────────────────────────┘

Robot Simulation The Physical AI software stack enables robots to learn in simulation before real-world deployment

NVIDIA Cosmos Foundation Model

Cosmos is NVIDIA's foundation model for Physical AI, designed to simulate realistic physical environments.

Key Capabilities

Feature Description Use Case
World Generation Create diverse 3D environments Training data generation
Physics Simulation Accurate physical interactions Robot behavior testing
Synthetic Data Photo-realistic rendering Computer vision training
Scenario Variation Randomize conditions Robust policy learning

Getting Started with Cosmos

# Note: API is illustrative based on announced capabilities
from nvidia_cosmos import CosmosClient, WorldConfig, AgentConfig

# Initialize Cosmos client
cosmos = CosmosClient(api_key="your-api-key")

# Define a warehouse environment
world_config = WorldConfig(
    template="indoor_warehouse",
    size=(50, 50, 10),  # meters
    features={
        "shelving_units": 20,
        "pallets": 50,
        "obstacles": "random",
        "lighting": "industrial"
    },
    physics={
        "gravity": 9.81,
        "friction_coefficient": 0.6
    }
)

# Create the simulation world
world = cosmos.create_world(world_config)

# Define robot agent
robot_config = AgentConfig(
    model="mobile_manipulator",
    sensors=["lidar", "rgbd_camera", "force_torque"],
    capabilities=["navigation", "manipulation"]
)

# Spawn robot in the world
robot = world.spawn_agent(robot_config, position=(0, 0, 0))

# Generate training scenarios
scenarios = world.generate_scenarios(
    task="pick_and_place",
    variations=1000,
    difficulty_range=(0.3, 0.9)
)

# Export for training
cosmos.export_dataset(
    scenarios,
    format="isaac_sim",
    output_path="./training_data"
)

Simulation Environments

NVIDIA Isaac Sim

Isaac Sim is the primary simulation environment for robotics development with NVIDIA hardware.

# Isaac Sim scene setup
from omni.isaac.core import World
from omni.isaac.core.robots import Robot
from omni.isaac.sensor import Camera, Lidar

# Create simulation world
world = World(stage_units_in_meters=1.0)

# Add ground plane
world.scene.add_default_ground_plane()

# Load a robot model
robot = world.scene.add(
    Robot(
        prim_path="/World/Robot",
        name="my_robot",
        usd_path="/path/to/robot.usd"
    )
)

# Add sensors
camera = Camera(
    prim_path="/World/Robot/camera",
    resolution=(640, 480),
    frequency=30
)

lidar = Lidar(
    prim_path="/World/Robot/lidar",
    rotation_frequency=10,
    horizontal_fov=360,
    vertical_fov=30
)

# Simulation loop
while simulation_running:
    world.step(render=True)

    # Get sensor data
    rgb_image = camera.get_rgb()
    depth_image = camera.get_depth()
    lidar_points = lidar.get_point_cloud()

    # Run your AI policy
    action = policy.predict(rgb_image, depth_image, lidar_points)

    # Apply action to robot
    robot.apply_action(action)

Gazebo (Open Source Alternative)

For those who prefer open-source tools:

# ROS 2 + Gazebo setup
import rclpy
from rclpy.node import Node
from geometry_msgs.msg import Twist
from sensor_msgs.msg import LaserScan, Image

class RobotController(Node):
    def __init__(self):
        super().__init__('robot_controller')

        # Publishers
        self.cmd_vel_pub = self.create_publisher(
            Twist, '/cmd_vel', 10
        )

        # Subscribers
        self.lidar_sub = self.create_subscription(
            LaserScan, '/scan', self.lidar_callback, 10
        )
        self.camera_sub = self.create_subscription(
            Image, '/camera/image_raw', self.camera_callback, 10
        )

        # Control loop
        self.timer = self.create_timer(0.1, self.control_loop)

    def lidar_callback(self, msg):
        self.latest_scan = msg

    def camera_callback(self, msg):
        self.latest_image = msg

    def control_loop(self):
        # Your AI policy here
        if hasattr(self, 'latest_scan') and hasattr(self, 'latest_image'):
            action = self.policy.predict(
                self.latest_scan,
                self.latest_image
            )

            cmd = Twist()
            cmd.linear.x = action.linear_velocity
            cmd.angular.z = action.angular_velocity
            self.cmd_vel_pub.publish(cmd)

def main():
    rclpy.init()
    controller = RobotController()
    rclpy.spin(controller)
    rclpy.shutdown()

Isaac Sim Environment Simulation environments like Isaac Sim provide realistic testing grounds for robotics

ROS 2 Integration Patterns

ROS 2 (Robot Operating System 2) is the standard framework for robotics development.

Project Structure

my_robot_project/
├── src/
│   ├── my_robot_description/    # Robot URDF/SDF models
│   │   ├── urdf/
│   │   ├── meshes/
│   │   └── config/
│   ├── my_robot_bringup/        # Launch files
│   │   ├── launch/
│   │   └── config/
│   ├── my_robot_control/        # Control nodes
│   │   ├── my_robot_control/
│   │   └── setup.py
│   └── my_robot_ai/             # AI components
│       ├── my_robot_ai/
│       │   ├── perception.py
│       │   ├── planning.py
│       │   └── policy.py
│       └── setup.py
├── docker/
│   └── Dockerfile
└── README.md

AI-Powered Navigation Node

# my_robot_ai/policy.py
import rclpy
from rclpy.node import Node
from nav_msgs.msg import OccupancyGrid, Path
from geometry_msgs.msg import PoseStamped
import torch
from .models import NavigationPolicy

class AINavigationNode(Node):
    def __init__(self):
        super().__init__('ai_navigation')

        # Load trained policy
        self.policy = NavigationPolicy.load('models/nav_policy.pt')
        self.policy.eval()

        # ROS interfaces
        self.goal_sub = self.create_subscription(
            PoseStamped, '/goal_pose', self.goal_callback, 10
        )
        self.map_sub = self.create_subscription(
            OccupancyGrid, '/map', self.map_callback, 10
        )
        self.path_pub = self.create_publisher(
            Path, '/planned_path', 10
        )

        self.get_logger().info('AI Navigation node initialized')

    def goal_callback(self, goal_msg):
        if not hasattr(self, 'current_map'):
            self.get_logger().warn('No map received yet')
            return

        # Prepare inputs for policy
        map_tensor = self.preprocess_map(self.current_map)
        goal_tensor = self.preprocess_goal(goal_msg)

        # Run inference
        with torch.no_grad():
            path_waypoints = self.policy(map_tensor, goal_tensor)

        # Convert to ROS Path message
        path_msg = self.waypoints_to_path(path_waypoints)
        self.path_pub.publish(path_msg)

    def map_callback(self, map_msg):
        self.current_map = map_msg

    def preprocess_map(self, map_msg):
        # Convert OccupancyGrid to tensor
        width = map_msg.info.width
        height = map_msg.info.height
        data = torch.tensor(map_msg.data).reshape(height, width)
        return data.unsqueeze(0).unsqueeze(0).float() / 100.0

    def preprocess_goal(self, goal_msg):
        return torch.tensor([
            goal_msg.pose.position.x,
            goal_msg.pose.position.y,
            goal_msg.pose.orientation.z,
            goal_msg.pose.orientation.w
        ]).unsqueeze(0)

    def waypoints_to_path(self, waypoints):
        path = Path()
        path.header.frame_id = 'map'
        path.header.stamp = self.get_clock().now().to_msg()

        for wp in waypoints:
            pose = PoseStamped()
            pose.header = path.header
            pose.pose.position.x = float(wp[0])
            pose.pose.position.y = float(wp[1])
            path.poses.append(pose)

        return path

From Simulation to Real-World Deployment

The biggest challenge in robotics is the "sim-to-real" gap—policies that work in simulation often fail in the real world.

Domain Randomization

Train with variations to build robust policies:

class DomainRandomizer:
    def __init__(self):
        self.randomization_params = {
            'lighting': {'intensity': (0.3, 1.5), 'color_temp': (3000, 7000)},
            'physics': {'friction': (0.4, 0.8), 'mass_scale': (0.9, 1.1)},
            'sensor': {'noise_std': (0.0, 0.05), 'dropout': (0.0, 0.1)},
            'geometry': {'scale': (0.95, 1.05), 'position_offset': (-0.02, 0.02)}
        }

    def randomize_environment(self, env):
        # Randomize lighting
        env.set_lighting(
            intensity=random.uniform(*self.randomization_params['lighting']['intensity']),
            color_temp=random.uniform(*self.randomization_params['lighting']['color_temp'])
        )

        # Randomize physics
        for obj in env.get_objects():
            obj.friction = random.uniform(*self.randomization_params['physics']['friction'])
            obj.mass *= random.uniform(*self.randomization_params['physics']['mass_scale'])

        # Add sensor noise
        env.sensor_noise_std = random.uniform(*self.randomization_params['sensor']['noise_std'])

        return env

# Training loop with domain randomization
randomizer = DomainRandomizer()

for episode in range(num_episodes):
    env = randomizer.randomize_environment(base_env)
    observation = env.reset()

    while not done:
        action = policy(observation)
        observation, reward, done, info = env.step(action)
        # Update policy...

Reality Gap Mitigation

class SimToRealAdapter:
    """Adapts simulation-trained policies for real-world deployment"""

    def __init__(self, sim_policy, calibration_data):
        self.sim_policy = sim_policy
        self.calibration = self.compute_calibration(calibration_data)

    def compute_calibration(self, data):
        """Learn systematic differences between sim and real"""
        # Compute observation normalization
        obs_mean = np.mean(data['observations'], axis=0)
        obs_std = np.std(data['observations'], axis=0)

        # Compute action scaling
        action_scale = data['real_action_scale'] / data['sim_action_scale']

        return {
            'obs_mean': obs_mean,
            'obs_std': obs_std,
            'action_scale': action_scale
        }

    def predict(self, real_observation):
        # Normalize observation to match simulation distribution
        normalized_obs = (real_observation - self.calibration['obs_mean']) / self.calibration['obs_std']

        # Get action from simulation-trained policy
        sim_action = self.sim_policy.predict(normalized_obs)

        # Scale action for real hardware
        real_action = sim_action * self.calibration['action_scale']

        # Apply safety limits
        real_action = np.clip(real_action, self.action_limits[0], self.action_limits[1])

        return real_action

Humanoid Robot Domain randomization helps bridge the gap between simulation and real-world deployment

LG's Humanoid Robot: A Case Study

At CES 2026, LG unveiled a humanoid robot designed for household tasks. Here's what we can learn from their approach:

Architecture Highlights

  1. Modular task system - Each task (laundry, dishes) is a separate module
  2. Hierarchical control - High-level planning separate from motor control
  3. Human interaction layer - Natural language understanding for commands
  4. Safety-first design - Multiple redundant safety systems

Simplified Task Architecture

class HouseholdRobot:
    def __init__(self):
        self.perception = PerceptionModule()
        self.planner = TaskPlanner()
        self.manipulator = ManipulationController()
        self.navigator = NavigationController()
        self.safety = SafetyMonitor()

    async def execute_task(self, natural_language_command: str):
        # Parse command
        task = await self.parse_command(natural_language_command)

        # Plan task sequence
        plan = await self.planner.create_plan(task, self.perception.get_scene())

        # Execute with safety monitoring
        for step in plan.steps:
            # Check safety before each step
            if not self.safety.is_safe_to_proceed():
                await self.handle_safety_stop()
                continue

            if step.type == 'navigate':
                await self.navigator.go_to(step.target)
            elif step.type == 'manipulate':
                await self.manipulator.execute(step.action)
            elif step.type == 'wait':
                await asyncio.sleep(step.duration)

            # Verify step completion
            if not await self.verify_step(step):
                await self.handle_step_failure(step)

    async def parse_command(self, command: str):
        # Use LLM to parse natural language to structured task
        response = await self.llm.parse(
            prompt=f"Parse this household task: {command}",
            output_format=TaskSchema
        )
        return response

Getting Started Resources

Development Environment Setup

# Install ROS 2 Humble (Ubuntu 22.04)
sudo apt update && sudo apt install -y curl gnupg lsb-release
sudo curl -sSL https://raw.githubusercontent.com/ros/rosdistro/master/ros.key -o /usr/share/keyrings/ros-archive-keyring.gpg
echo "deb [arch=$(dpkg --print-architecture) signed-by=/usr/share/keyrings/ros-archive-keyring.gpg] http://packages.ros.org/ros2/ubuntu $(lsb_release -cs) main" | sudo tee /etc/apt/sources.list.d/ros2.list > /dev/null
sudo apt update && sudo apt install -y ros-humble-desktop

# Install simulation tools
sudo apt install -y ros-humble-gazebo-ros-pkgs
pip install nvidia-isaac-sim  # Requires NVIDIA GPU

# Install AI/ML dependencies
pip install torch torchvision
pip install stable-baselines3  # RL algorithms
pip install gymnasium  # Environment interface
  1. Week 1-2: ROS 2 basics (nodes, topics, services)
  2. Week 3-4: Simulation with Gazebo
  3. Week 5-6: Computer vision for robotics
  4. Week 7-8: Navigation and path planning
  5. Week 9-10: Manipulation basics
  6. Week 11-12: RL for robotics
  7. Ongoing: Isaac Sim and Cosmos integration

Key Takeaways

  1. Physical AI is software - You don't need a hardware background to start
  2. Simulation is essential - Train and test before touching real robots
  3. ROS 2 is the standard - Learn it to work in the ecosystem
  4. Domain randomization helps bridge sim-to-real gap
  5. Start with navigation - It's the most accessible entry point

Resources

Interested in robotics development? Join the CODERCOPS community to connect with other developers exploring Physical AI.

Comments