Experimental Leg Inverse Dynamics Learning of Multi-legged Walking Robot

Rough terrain locomotion is a domain where multi-legged robots benefit from their relatively complex morphology compared to the wheeled or tracked robots. Efficient rough terrain locomotion requires the legged robot sense contacts with the terrain to adapt its behavior and cope with the terrain irregularities. Usage of inverse dynamics to estimate the leg state and detect the leg contacts with the terrain suffers from computational complexity. Furthermore, it requires a precise analytical model identification that does not cope with adverse changes of the leg parameters such as friction changes due to the joint wear, the increased weight of the leg due to the mud deposits, and possible leg morphology change due to damage. In this paper, we report the experimental study on the locomotion performance with machine learning-based inverse dynamics model learning. Experimental examining three different learning models show that a simplified model is sufficient for leg collision detection learning. Moreover, the learned model is faster for calculation and generalizes better than more complex models when the leg parameters change.