Grip Quality

Grip quality research focuses on understanding and improving how robots and humans grasp objects, aiming to achieve stable and robust manipulation. Current research employs machine learning models, including neural networks and LSTM architectures, to predict grip stability from various data sources like tactile sensor readings, 3D motion capture, and even simulated environments. This work is crucial for advancing robotics, particularly in areas like industrial automation and assistive technologies, by enabling more reliable and dexterous manipulation capabilities. Furthermore, understanding the mechanics of grip informs the design of improved gripper technologies, such as soft robotic grippers utilizing granular jamming.