Back in Action Planning in Python, we wrote a simple A* based planner that could solve the problem of boiling water, given a scenario with initial conditions, actions the AI agent can take, and preconditions for those actions. The result was:

Path:
pick up pot
move to sink
turn on faucet
wait
turn off faucet
move to stove
put down pot
turn on stove
goal

In Part 2, we're going to rewrite it in Rust.

Automated planning is a field of AI techniques for determining how to transform the world from an initial state to a goal state. This guide implements a form of classical planning in which we represent the problem domain as a set of state variables that are modified by each action taken.

Now that we have a basic inverse kinematic model, the sky's the limit. Wait. The horsepower of a Raspberry Pi Model A is a pretty good limit. In this video, the eye vector is tracing out a circle, trying to hold 1 second per circle.