Robots used to copy actions from videos without truly understanding how the world changes, so they often messed up long, multi-step jobs.
Robots often get confused on long, multi-step tasks when they only see the final goal image and try to guess the next move directly.
This paper shows how to make home-helper robots better at long, multi-step chores by smart training on diverse tasks and by polishing the model after training using its own best attempts.
Robots often act like goldfish with short memories; HiF-VLA fixes this by letting them use motion to remember the past and predict the future.