Inverse Dynamic

Inverse dynamics, the process of determining the forces and torques required to achieve a desired robot motion, is a crucial area of robotics research focused on improving control accuracy and efficiency. Current research emphasizes developing accurate and computationally efficient inverse dynamics models, employing techniques like Gaussian processes, neural networks (including deep kernel models and recurrent architectures like LSTMs), and hybrid approaches combining data-driven methods with physics-based models. These advancements are significant for enhancing robot manipulation capabilities, particularly in handling heavier payloads and complex tasks, and improving human-robot interaction by enabling more compliant and adaptable control strategies.