We introduce a functional gradient descent trajectory optimization algorithm
for robot motion planning in Reproducing Kernel Hilbert Spaces (RKHSs).
Functional gradient algorithms are a popular choice for motion planning in
complex many-degree-of-freedom robots, since they (in theory) work by directly
optimizing within a space of continuous trajectories to avoid obstacles while
maintaining geometric properties such as smoothness. However, in practice,
functional gradient algorithms typically commit to a fixed, finite
parameterization of trajectories, often as a list of waypoints. Read More