Locomotion

Footstep Planning

Footstep planning is the problem of selecting a sequence of foot placement positions, orientations, and timings that carry a legged robot to a goal while respecting kinematic reach, balance constraints, and terrain validity. Methods include graph search over discrete step sets, mixed-integer and nonlinear optimization over stepping stones, and learned policies that propose footholds from perception. It is typically layered above whole-body and below global path planning.

Why it matters for physical AI

Where a robot may step is the fundamental contract between perception and legged control; robust foothold selection on stairs, gaps, and rubble is what turns locomotion policies into navigation capability.