Provides a set of utilities for interactive visualization with Drake's Meshcat, including `StartMeshcat` for initializing a Meshcat server, `DisplayMeshcat` for displaying the visualizer in notebooks, `MeshcatSliders` for adding interactive sliders to control simulation parameters, `WsgButton` and `StopButton` for adding buttons, `AddMeshcatTriad` for adding coordinate axes, and `plot_mathematical_program` for visualizing optimization problems. This module encapsulates common patterns for setting up Meshcat visualizers and is reusable in any Drake-based project requiring interactive 3D visualization.

Provides custom PyPlot visualizer subclasses that extend Drake's visualization capabilities: `AdvanceToAndVisualize` for stepping through simulation time with visualization, `AnimatePositionTrajectory` for creating animations of position trajectories, and `HistogramVisualizer` for visualizing distributions. These are self-contained components that can be extracted and used in other Drake projects for 2D plotting and animation.

Implements a `Grader` class for autograding Jupyter notebook exercises. It provides methods to grade student outputs against expected results and generate results in a format compatible with Gradescope. This is a self-contained pattern that can be extracted and used in any educational context requiring automated assessment of notebook-based assignments.

Implements `UavEnvironment`, a class for generating 3D grid worlds with buildings, walls, doors, trees, and other obstacles for UAV motion planning. It uses Drake's GraphOfConvexSets for planning collision-free paths. The environment generation is parameterized and randomized, making it reusable for different UAV planning scenarios. It demonstrates non-obvious skill in combining procedural generation with convex optimization.