Perslis Motion
PERSLIS MOTION · FLIGHT DEMO

The floor at the controls
of a drone.

A recorded run of the flight console, replayed panel for panel. The drone flies the route it learned by trial and failure on the course from the open-source jev-drone project. The learned plan sets the speed; the stop-distance limit and the reflexes are applied after it, so they can only slow it down. There is no neural network anywhere in the loop.

What you are watching

Watch it learn from nothing

The replay above flies a route that is already learned. The recording below starts from a wiped memory: seven rounds take the cost from 69.39 to 51.52, two changes kept and five reverted. Contacts happen while it learns; each costs 15 s and a round that adds them is thrown out.

Results, with the caveats

On held-out starting positions, run once after the rules were frozen, the floor flew the whole course 10 of 10 times with 0 contacts. The unmodified baseline finished 1 of 10.

Caveats that always go with that number: the course is identical in every run and only the start pose varies, so this is weak evidence of generalisation; it is simulation, not flight; the upstream project is pinned and unmodified, and we did not re-run anyone else's system.

How it connects

This is the same idea as a fail-safe model: the learner changes what the drone tries, never what it is allowed to do. The decision path is a symbolic flow: engine state becomes facts, the rules choose, the actuator acts. The flight floor itself is described on Flight and The engine. Peel is, to our knowledge, the first fail-safe model.