Robot Simulation

Test, validate, and train robot behaviors in realistic virtual environments before deploying to real hardware - saving time, cost, and risk.

Why Simulate?

Real robots are expensive, fragile, and slow to test. Simulation allows you to iterate thousands of times faster, test dangerous scenarios safely, and train AI models with unlimited data. The sim-to-real transfer gap is shrinking rapidly with modern physics engines and domain randomization techniques.

Popular Simulators

Gazebo with ROS 2

# Launch a robot in Gazebo
ros2 launch gazebo_ros gazebo.launch.py world:=empty.world

# Spawn a robot model
ros2 run gazebo_ros spawn_entity.py \
    -entity my_robot \
    -file /path/to/robot.urdf

# Interact with simulated sensors
ros2 topic echo /camera/image_raw
ros2 topic echo /scan  # LiDAR data

Reinforcement Learning in Simulation

import gymnasium as gym
import mujoco

# Create a MuJoCo-based RL environment
env = gym.make('Ant-v4', render_mode='human')
obs, info = env.reset()

for _ in range(10000):
    action = env.action_space.sample()  # Replace with RL policy
    obs, reward, terminated, truncated, info = env.step(action)
    if terminated or truncated:
        obs, info = env.reset()

env.close()

Sim-to-Real Transfer

The biggest challenge in simulation is the reality gap - differences between simulated and real-world physics, visuals, and dynamics. Key techniques to bridge this gap:

• Domain randomization: Randomize textures, lighting, physics parameters, and sensor noise during training
• System identification: Carefully calibrate simulation parameters to match real hardware
• Domain adaptation: Use transfer learning to adapt models trained in simulation to real data
• Progressive training: Start in simulation, fine-tune on limited real-world data

Simulator Comparison