Learning Contact Representation for Leg Odometry

Leg odometry relies on the assumption that the velocity of a foot in stance is zero with respect to the world, which makes accurate contact detection a critical subproblem. Many approaches classify contact using ground reaction force (GRF) sensors at the foot tip, yet these sensors are not available on all legged robots and remain unresponsive to disturbances such as slippage while the foot is still in contact. Approaches without GRF sensing instead rely on heuristic pseudo-labels, model-based foot-velocity assumptions, or supervised contact learning. In this study, we propose a self-supervised representation learning framework for contact detection that uses only standard proprioceptive sensing (joint encoders and IMU) and requires neither force sensors nor contact labels. A denoising autoencoder is trained on kinematic features, a two-component Gaussian Mixture Model is fitted in the learned latent space, and its posterior is used as a continuous stance belief that scales the zero-velocity measurement covariance of an error-state Kalman filter. Experiments in simulation and on real-world terrain show that our framework yields the lowest odometry error among supervised and probabilistic baselines, and that higher pseudo-label classification accuracy does not translate into better downstream odometry. Our code and dataset are publicly available.