What tuning notes actually survive across sessions, payload changes, and new tools when working with OpenArm MIT control?
This thread is for advanced OpenArm builders and integrators working with MIT control gains.\n\nWhat exact notes do you record when adjusting gains, and what patterns tell you a setting is unsafe even before a full failure happens?\n\nIf you reply, include one observable symptom and one rollback step.
The mistake is not aggressive tuning by itself. It is aggressive tuning without enough context to safely undo the change the next day.
Good tuning notes always include loop rate, payload state, visible oscillation, and the first change that made the run feel unstable.
If you already have a tuning worksheet, share the smallest version another team could adopt immediately.