The team designed an algorithm that allows an autonomous ground vehicle to improve its existing navigation systems by watching how a human driver remote-controlled the same vehicle, calling the approach ‘adaptive planner parameter learning from demonstration’ – APPLD.
The approach was compared with current autonomous navigation systems built around hand-tuned algorithms – which rely on the prescience of designers and can trip-up when presented with unforeseen environments. These improve gradually through trial, error and re-tuning.
