Learning library

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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148 lessons to explore

Foundations first

  1. Foundations / 11 min read

    Vision-language-action models: from observations to robot commands

    Learn how vision-language-action models connect images and instructions to robot actions. Compare token and continuous outputs, then inspect how units, frames, and timing change a synthetic command.

  2. 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.

  3. Foundations / 10 min read

    ROS 2 topics, services, and actions: choose the right exchange

    Choose topics for streams, services for short requests, and actions for goals with progress and results. Follow odometry, a reset, and a navigation cancellation.

  4. Foundations / 12 min read

    Visual odometry vs SLAM: motion, maps, and metric scale

    Compare visual odometry with SLAM, understand what local maps and loop closures add, and use a pinhole-camera inspector to see why monocular images cannot establish metric scale alone.

  5. Foundations / 18 min read

    Trajectory error metrics: compare full paths, frames, and drift

    Compare ATE, RPE, alignment, and timing with an interactive trajectory lab and downloadable Python evaluator. Inspect timestamp matches, missing coverage, and a misleading zero-error fit.

  6. Foundations / 10 min read

    Robot autonomy and teleoperation: who makes each decision?

    Compare direct teleoperation, supervisory control, shared autonomy, and bounded task autonomy. Follow one grasping task, calculate communication delay, and read intervention records.

  7. Foundations / 12 min read

    A 2D lidar scan model: ranges, rays, and visible surfaces

    Turn lidar angles and ranges into points, find the nearest visible surface with ray casting, and distinguish a missing return from an invalid reading. Inspect occlusion and coordinate frames in an interactive room.

  8. Foundations / 11 min read

    Occupancy grid mapping: update free, occupied, and unknown space

    Build a small occupancy grid from supplied range readings. Follow free-space and hit updates, calculate log odds, and distinguish unseen cells from conflicting evidence.

  9. Foundations / 13 min read

    Particle filter localization: predict, weight, and resample

    Estimate a robot's position with weighted particles. Follow motion prediction, sensor likelihoods, effective sample size, and systematic resampling, then test an initial belief that misses the robot entirely.

  10. Foundations / 13 min read

    Extended Kalman filter: localize a robot with range and bearing

    Predict a robot's nonlinear motion, linearize with Jacobians, and correct its pose from a known landmark. Inspect angle wrapping, covariance, and a poor initial heading that challenges the local approximation.

  11. Foundations / 13 min read

    Pose-graph optimization: fit robot positions and headings together

    Optimize a small planar pose graph with real nonlinear updates. Calculate relative-pose residuals, anchor the world frame, and compare measurement fit with error against a synthetic reference.

  12. Foundations / 12 min read

    ICP failure cases: when a smaller residual gives the wrong pose

    Diagnose ICP failure cases with repeated geometry, partial overlap, and unmatched points. Compare the fitting residual with a known pose error, change the starting estimate, and see which evidence each metric uses.