Online Learning

This work describes the development and testing of a data-driven hydrodynamic model for quadruped robots designed for adaptive and intelligent interaction in dynamic environments. To effectively manage and interpret sensor data, we employ Gaussian Process Regression (GPR) to model the underlying uncertainties in fluid-structure interactions, allowing for more precise predictions in complex and varying environments. The probabilistic nature of GPR enables quadruped robots to handle noisy data and provide robust, uncertainty-aware decision-making strategies.