HAPmamba: Linear-Time Sequence Modeling for Terrain Classification by Legged Robots

This paper proposes a neural network model for terrain classification using force/torque signals registered with sensors mounted on robot feet. The proposed model is based on the latest architecture for Linear-Time Sequence Modeling using Selective State Spaces, called Mamba. We obtained lightweight models with very low inference times, which are two times faster than comparable transformer-based solutions. We evaluated HAPmamba alongside other state-of-the-art approaches, and while the classification measures are comparable, HAPmamba is the fastest among all evaluated models.