Stuff Made Here upgrades robot golf club with MuJoCo physics engine

Building a robot that hits a golf ball straight is a solved problem; building one that understands the chaotic geometry of a mini-golf course requires a massive software upgrade. The challenge lies in moving beyond simple direct-line aiming to a system that can simulate the physics of banks, bounces, and curved surfaces. To accomplish this, the project pivots from basic trigonometry to a full-scale physics simulation. By creating a digital twin of the physical environment, the club can "think" through thousands of potential outcomes before the head even makes contact with the ball. This process demystifies the complex interaction between the ball's spin, surface friction, and the specific topography of the green.

Prerequisites and technical foundations

To implement a similar simulation-driven hardware project, you need a firm grasp of several core concepts:

• Python Programming: For scripting the simulation logic and handling data from motion capture systems.
• Rigid Body Physics: Understanding how objects interact, specifically how friction and restitution (bounciness) affect motion.
• Spatial Geometry: Knowledge of coordinate transforms to align the physical world with the virtual one.
• Motion Capture Integration: Familiarity with systems like OptiTrack to feed real-time positional data into your software.

Key Libraries and Tools

• MuJoCo: A high-performance physics engine used for robotics and biomechanical research. It handles the complex contact dynamics of the golf ball hitting walls.
• Stochastic Solvers: Mathematical frameworks used to find optimal parameters through random but systematic searching.
• Brilliant: An interactive learning platform used to master the underlying math and geometric transforms necessary for this build.

Code Walkthrough: From scanning to simulation

The implementation begins with environmental mapping. Since CAD files rarely match reality, a custom 3D scanner—using a constellation of reflective markers—traces the hole's geometry. This data is fed into MuJoCo.

# Conceptualizing the simulation loop
import mujoco

def simulate_shot(club_angle, swing_speed):
    # Initialize the model with scanned course geometry
    model = mujoco.load_model_from_path("minigolf_hole.xml")
    data = mujoco.MjData(model)

    # Apply initial conditions to the virtual club
    data.qvel[club_joint] = swing_speed
    data.qpos[head_joint] = club_angle

    # Step the simulation until the ball stops or enters the hole
    for _ in range(MAX_STEPS):
        mujoco.mj_step(model, data)
        if ball_in_hole(data):
            return True
    return False

The real heavy lifting occurs during precomputation. Because simulating every possible shot in real-time is too slow, the system runs an exhaustive search of all speeds and angles when the ball is placed. This results in a lookup table of "scoring shots" that the club can reference instantly during a physical swing.

Syntax Notes and Optimization

When working with physics simulations, Parameter Tuning is the primary hurdle. You will encounter the "Genie Problem" where the solver maliciously complies with your code—like increasing friction to infinity because a stationary ball technically never misses its target. To combat this, ensure your cost functions reward the total path accuracy, not just the final destination. Additionally, offloading computation to a high-thread-count workstation (the "Beast Computer") is essential when processing tens of thousands of simulations within a few minutes.

Practical Examples and Calibration

The system's effectiveness is best seen on the "Wavy Hole," where the ball must traverse varying elevations. The physics engine accounts for thermal expansion; if the workshop warms up, the camera tripods expand, shifting the coordinate system. To fix this, use reference markers on the course to dynamically calculate a rotation matrix that realigns the simulation with the physical world every time the program runs.

Tips and Gotchas

• Manual vs. Automated: Sometimes a stochastic solver fails where human intuition succeeds. Building a manual GUI to tweak parameters (friction, damping) can save weeks of debugging.
• Contact Dynamics: A golf ball doesn't just reflect off a wall; it skids and jumps. If your simulation looks like "garbage," check your contact friction settings in MuJoCo.
• Speed Sensitivity: Lower speeds often exhibit chaotic behavior. If a shot is unreliable, increasing the swing velocity often stabilizes the ball's path by minimizing the impact of minor surface imperfections.