Slip and crumple detection is essential for performing robust manipulation tasks with a robotic hand (RH) like remote surgery. It has been one of the challenging problems in the robotics manipulation community. In this work, we propose a technique based on machine learning (ML) based techniques to detect the slip, and crumple as well as the shape of an object that is currently held in the robotic hand. We proposed ML model will detect the slip, crumple, and shape using the force/torque exerted and the angular positions of the actuators present in the RH. The proposed model would be integrated into the loop of a robotic hand(RH) and haptic glove(HG). This would help us to reduce the latency in case of teleoperation

翻译：滑动与褶皱的检测对于完成如远程手术等需机器人手部参与的鲁棒操作任务至关重要，这一直是机器人操作领域具有挑战性的问题之一。本研究提出一种基于机器学习的技术，用于检测当前机器人手部所持物体的滑动、褶皱及形状。我们提出的ML模型将利用机器人手部执行器施加的力/力矩及关节角位置来检测滑动、褶皱和形状。该模型将集成到机器人手部与触觉手套的闭环控制系统中，有助于减少遥操作场景下的延迟。