Papers

Experience-Driven Continual Learning of Terrain Traversability for Quadruped Robots

This preprint presents a quadruped traversability system that predicts five foot-contact outcomes from pre-contact camera images using a frozen DINOv3 backbone and an evidential regressor, then continually updates the model through replay with a historical validation gate. On a sequential real-robot stream across three unseen surface types, the gate cut anchor negative log-likelihood degradation by 23.1% versus replay without the gate while keeping new-terrain adaptation nearly equal. It matters for legged robots that must operate safely on unfamiliar ground without forgetting earlier experience.