  Under-canopy agricultural robots require robust navigation capabilities to
enable full autonomy but struggle with tight row turning between crop rows due
to degraded GPS reception, visual aliasing, occlusion, and complex vehicle
dynamics. We propose an imitation learning approach using diffusion policies to
learn row turning behaviors from demonstrations provided by human operators or
privileged controllers. Simulation experiments in a corn field environment show
potential in learning this task with only visual observations and velocity
states. However, challenges remain in maintaining control within rows and
handling varied initial conditions, highlighting areas for future improvement.
