Diffeomorphic Hidden-State Steering: Improving Imitation Learning Policy Robustness through Expert Hidden-State Geometry
The method uses expert hidden-state geometry to improve a trained imitation learning policy's robustness during testing by applying variance-dependent rescaling in nonlinear coordinates. This allows steering directions to adapt based on the current hidden state, leading to higher task success under observation noise compared to standard methods.