Inertial Parameter Identification

Inertial parameter identification focuses on accurately determining a robot's mass and inertia properties, crucial for precise simulation, control, and safe interaction with the environment. Current research emphasizes developing efficient algorithms, including those based on neural networks (e.g., recurrent inertial graph-based estimators) and Kalman filters, that can estimate these parameters using readily available sensor data like joint torques, even in the absence of direct force measurements or high-quality CAD models. This work is significant because accurate inertial parameters improve robot performance in various applications, from locomotion and manipulation to autonomous navigation and object interaction, particularly in scenarios involving contact or external loads. The development of computationally efficient methods is a key focus, enabling real-time adaptation and improved robustness in dynamic environments.