Recently, a study from the Swiss École Polytechnique Fédérale de Lausanne (EPFL) made its debut at the IEEE International Conference on Robotics and Automation in Rotterdam. This research aims to explore how robotic hands can break existing limitations to grasp more objects. The research team noted that deep learning models significantly enhanced the dexterous manipulation capabilities of multi-fingered hands, but the grasping guided by contact information in cluttered environments has not yet been fully explored. To address this issue, the researchers designed an inspired hand that can bend backward to pick up various objects and detach itself to climb.