A legged robot on planetary regolith faces a sensing problem that cameras cannot solve. Sand pits and solid ground look identical from above. Visual terrain classification fails exactly where it matters most: at the boundary between safe footing and subsurface collapse.
The solution is proprioceptive: the robot learns terrain traversability from leg-terrain interaction forces during locomotion. Each footstep produces force signatures — ground reaction profiles, compliance estimates, slip detection — that encode the mechanical properties vision cannot access. A Gaussian process model built from these signatures predicts traversability for unvisited terrain, guiding the robot toward regions of low uncertainty and high safety.
The key insight is not that force sensing supplements vision. It's that locomotion and surveying are the same action. Each step taken for the purpose of moving also generates the data needed to evaluate the terrain. There is no separate measurement phase. The robot doesn't stop to probe the ground and then decide whether to cross it. It learns the ground's load-bearing capacity in the act of bearing load on it.
This collapses the exploration-exploitation boundary. Exploration (learning about terrain) and exploitation (traversing terrain) are not competing objectives to be balanced. Every exploitative step is also an exploratory measurement. The tradeoff that dominates theoretical formulations dissolves when the sensing modality is intrinsic to the task.
The through-claim: when the sensor is the actuator, perception is free. Vision separates seeing from acting — the camera observes, then the legs respond. Proprioception couples them. The leg that steps is the leg that measures. The measurement has zero marginal cost because it is a byproduct of the action it informs. On deformable terrain where vision fails, the cheapest sensor is the one you already have: the body doing the work.