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Understand how a robot works.

Build working knowledge of robotics and machine learning through examples you can change, calculations you can check, and data you can inspect.

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10 lessons found

Foundations first

  1. Foundations / 8 min read

    Conditional probability: reading a robot sensor

    Learn conditional probability with an interactive robot sensor example. Work through the formula, Bayes' rule, base rates, and common mistakes using counts.

  2. Foundations / 9 min read

    Bayesian inference: learning a robot's grasp success rate

    Learn Bayesian inference through a robot grasp example. Update a Beta prior with successes and failures, then distinguish uncertainty from prediction.

  3. Foundations / 10 min read

    Logistic regression: turn a score into a probability

    Explore logistic regression with synthetic robot observations. Calculate sigmoid probabilities and log loss, then change a threshold and inspect the confusion matrix.

  4. Foundations / 9 min read

    Markov chains: transitions and stationary distributions

    Learn Markov chains with a robot example. Explore transition matrices, stationary distributions, periodic chains, and absorbing states with an interactive model.

  5. Foundations / 9 min read

    Laplace distribution: model sensor error and tolerance

    Learn the Laplace distribution through sensor error. Explore location, scale, density, interval probabilities, and the link between absolute error and median fitting.

  6. Foundations / 13 min read

    SLAM in robotics: estimate a robot’s path and its map

    Understand SLAM in robotics by solving a small landmark problem. Compare localization, mapping, and joint estimation, then see how anchoring and measurement weights change the answer.

  7. Foundations / 10 min read

    Covariance and uncertainty ellipses: read the shape of error

    Read a covariance matrix as a geometric description of spread. Rotate an uncertainty ellipse, compare a Gaussian model with a finite sample, and calculate what a 95% contour means.

  8. Foundations / 12 min read

    Kalman filter: predict position, then correct with measurements

    Work through a scalar Kalman update, then estimate position and velocity from noisy readings. Explore missing measurements, covariance, and a biased sensor that defeats the filter’s assumptions.

  9. Foundations / 11 min read

    Diffusion Policy: generate robot actions by denoising

    See how Diffusion Policy represents several possible actions, learns to remove noise, and executes action chunks. Follow an exact teaching denoiser and separate sampling steps from robot time.

  10. Foundations / 12 min read

    LQG control: connect a Kalman estimate to LQR feedback

    Control a cart from noisy position readings. Connect Kalman filtering to LQR, compare matched disturbances, and test what changes when the model is wrong.