Control SLIP

Control SLIP (Slippage) research encompasses methods for predicting, preventing, and mitigating unwanted slippage in various systems, from robotic locomotion and manipulation to securing intellectual property in large language models. Current efforts focus on developing robust models, often employing deep learning architectures like convolutional neural networks and transformers, to estimate slip conditions using diverse sensor data (tactile, visual, barometric) and adapt control strategies accordingly. These advancements are crucial for improving the reliability and efficiency of robots in complex environments and for enhancing the security of sensitive AI models.