Inertial Parameter

Inertial parameter identification focuses on accurately determining the mass distribution and moments of inertia of objects, crucial for precise robot control and simulation, especially in dynamic interactions. Current research emphasizes efficient algorithms, often leveraging machine learning techniques like reinforcement learning and time series clustering, to overcome challenges posed by limited sensor data (e.g., absence of force/torque sensors) and noisy measurements in real-world scenarios. This work is significant for improving robot dexterity and safety in human-robot collaboration and diverse applications such as legged locomotion, spacecraft operations, and object manipulation, where accurate dynamic models are essential.