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04:57 AM

Topic 5 — Sim-to-Real Transfer & Capstone Digital Twin

By now you have a functioning digital twin across Gazebo, Isaac Sim, and optionally Unity. This topic focuses on the sim-to-real gap—why systems that work in simulation often fail on hardware—and how to reduce that gap using system identification, domain randomization, and rigorous validation protocols. It concludes by tying these ideas directly into your capstone humanoid project.

5.1 The Sim-to-Real Challenge​

Even high-fidelity simulators cannot perfectly reproduce reality. Common sources of mismatch:

  • Visual:
    • Real cameras have complex noise, blur, lens artifacts, and white balance shifts.
    • Environments are messy, cluttered, and poorly lit compared to curated virtual scenes.
  • Physics:
    • Friction coefficients vary with wear, humidity, and surface contamination.
    • Contact models (soft vs. hard, compliant vs. rigid) are approximate.
  • Dynamics:
    • Actuators have latency, backlash, and saturation that are hard to model.
    • Cables, belts, and flexibilities introduce unmodeled compliance.
  • Sensors:
    • Drift, bias, latency, and calibration errors accumulate over time.
  • Actuators & Control:
    • Nonlinearities, dead zones, hysteresis, and thermal effects.

Sim-to-real transfer asks: Can a policy or algorithm trained/tuned in simulation perform acceptably on the real robot, with minimal additional tuning?

5.2 System Identification: Measuring Reality​

System identification is the process of measuring physical parameters so that your simulation matches your robot more closely.

Parameters to identify:

  • Mass and inertia of links (if CAD is unavailable or inaccurate).
  • Friction (static and kinetic) for key contact surfaces (feet, grippers, joints).
  • Damping (viscous and Coulomb) in joints and actuators.
  • Compliance in joints, gearboxes, and structures.
  • Delays in sensor readings and actuator responses.

Typical methods:

  • Direct measurement:
    • Weigh components.
    • Measure geometry with calipers or CAD.
  • Experimental tests:
    • Free-fall or pendulum tests to estimate inertia and damping.
    • Sliding tests to estimate friction coefficients.
    • Step-response tests to measure actuator latency and gain.
  • Optimization-based fitting:
    • Run the same trajectory in sim and real.
    • Adjust parameters (mass, friction, damping) to minimize error between trajectories.

Once identified, update:

  • URDF inertial properties (for kinematics and some simulators).
  • SDF or USD physics parameters (for Gazebo and Isaac Sim).

5.3 Domain Randomization: Embracing Uncertainty​

Instead of trying to match reality exactly, domain randomization accepts that:

  • The real world is variable and uncertain.
  • It is better for policies to be robust across many plausible worlds.

Randomization axes:

  • Visual:
    • Textures, colors, lighting conditions, background clutter.
  • Sensor:
    • Noise levels, bias, latency, resolution.
  • Physics:
    • Mass, friction, restitution, joint damping.
  • Environment:
    • Object positions, shapes, and sizes.

Strategy:

  • Start from a realistic baseline (system-identified parameters).
  • Define sensible parameter ranges (not wildly unrealistic).
  • Sample parameters each episode or batch of synthetic data generation.

The outcome: controllers and perception models that generalize better when moved from sim to real.

5.4 Validation Protocol Before Hardware​

Before running new behaviors on expensive humanoid hardware, you should follow a staged validation protocol:

  1. Physics validation:
    • Does the simulated robot stand, walk, and manipulate objects in physically plausible ways?
    • Do contact forces and trajectories look similar to real logs (if available)?
  2. Control validation:
    • Can controllers stabilize the robot in the digital twin?
    • Are control loops running at the intended frequency (e.g., 200–500 Hz for balance)?
  3. Perception validation:
    • Do perception algorithms achieve target metrics (IoU, AP, trajectory error) on simulated data?
    • Have they been tested on a small amount of real data?
  4. Safety validation:
    • Are joint limits, velocity limits, and torque limits enforced?
    • Are there collision checks and emergency stop conditions?
  5. Performance validation:
    • Does the robot achieve the task in simulation at roughly the desired speed and robustness?

Staged deployment to hardware:

  • Stage 1 – Static tests:
    • Power up actuators with tight safety limits.
    • Verify the robot can stand or hold a pose without instability.
  • Stage 2 – Slow motion:
    • Execute trajectories at 10–20% speed.
    • Monitor joint errors and contact forces.
  • Stage 3 – Nominal motion:
    • Run at intended speed.
    • Continue to monitor metrics.
  • Stage 4 – Edge cases:
    • Introduce small disturbances and obstacles.
    • Verify recovery behaviors and safety margins.

Always maintain a rollback plan: if something looks wrong, fall back to a known-safe mode (e.g., freeze joints, sit down, or power off with brakes).

5.5 Capstone Integration: Digital Twin for the Autonomous Humanoid​

Your capstone project is an Autonomous Humanoid that navigates, perceives, and manipulates in human environments. The digital twin you built in this chapter should meet these requirements:

  • Robot model:
    • Humanoid URDF (from Chapter 2) imported into Gazebo and (optionally) Isaac Sim/Unity.
    • Correct kinematic chain and joint limits.
  • Physics:
    • Reasonable mass and inertia for major links.
    • Tuned friction and damping for feet and hands.
    • Stable walking and manipulation in simulation.
  • Sensors:
    • RGB-D camera(s) with intrinsics matching planned hardware.
    • IMU and (optional) LiDAR with realistic noise models.
    • Topics and frames consistent between simulation and real robot plans.
  • ROS 2 architecture:
    • Nodes for perception, planning, and control communicated through well-defined topics/services/actions.
    • Simulation-time and real-time clocks understood and handled correctly.
  • Ground truth & logging:
    • Mechanisms to log trajectories, sensor data, and ground truth (rosbags, Isaac Sim datasets).

During Chapters 4 and beyond, you will:

  • Train and evaluate perception models using data from your digital twin.
  • Develop motion planners and controllers tested first in Gazebo/Isaac Sim.
  • Incrementally port the same ROS 2 graph from simulation to real hardware.

The only components that should change when you move from sim to real are:

  • Driver-level interfaces (Gazebo/Isaac Sim plugins → hardware drivers).
  • Some parameter files (e.g., noise levels, friction, control gains).

Your higher-level algorithms and architecture should remain the same.

5.6 Hands-On Lab: End-to-End Validation Experiment (Obstacle Course)​

This capstone preparation lab ties together all of Chapter 3.

Scenario​

Your humanoid navigates a simple obstacle course world, avoiding obstacles and reaching a goal location.

Tasks​

  1. Environment:
    • Build a Gazebo world with floor, walls, and multiple obstacles (boxes, cylinders).
    • Configure realistic materials and friction for floor and obstacles.
  2. Perception system:
    • Simulate an RGB-D camera (RealSense-like settings).
    • Run a SLAM or localization node on simulated data.
    • Implement a simple obstacle detector (e.g., depth thresholding or learned model).
  3. Planning system:
    • Use a path planner (e.g., Nav2 or custom) to generate walking paths.
    • Ensure planner respects obstacles detected from perception.
  4. Control system:
    • Use a walking controller to execute paths (can be simplified to planar motion at this stage).
    • Ensure stability and collision-free execution in the digital twin.
  5. Validation:
    • Run at least 10 trajectories from start to goal under deterministic conditions.
    • Measure:
      • Success rate (reaching goal without collision).
      • Planning time.
      • Control tracking error (planned vs. executed path).
    • Log all runs with ros2 bag or equivalent.

Success Criteria​

  • The robot reaches the goal in all test runs without collisions in simulation.
  • Planning and control loops run at expected frequencies.
  • Logs are sufficient to analyze performance and potential failure modes.

Use the results to create a risk assessment:

  • What might still go wrong on the real robot (slippage, sensing failures, latency)?
  • What additional safeguards or experiments are needed before hardware trials?

This lab marks the transition from simulation-focused development (Chapter 3) to perception and planning (Chapter 4), using your digital twin as the proving ground for everything that follows.