Robots often move inefficiently due to slow mathematical computations. Now, a new method called Web of Affine Spaces (WASP) Optimization, developed by Yale’s Professor Danny Rakita and his team, dramatically accelerates how robots calculate derivatives.
By reusing information from prior computations, the WASP approach is seven to ten times faster than traditional methods, allowing robots to react more quickly and plan further ahead. This speed-up enables robots to navigate complex environments gracefully, proactively avoiding obstacles and paving the way for more agile, precise machines in everyday settings.
