In order to obtain a high-precision contact model that can properly describe the target soft tissue, this paper proposes a hybrid soft contact model based on a clustering algorithm ART-II, which selects the most suitable soft contact model according to the surgical environment. The least-square method is used to identify the parameters of the model online. In the experiments, different parts of animal tissues were used as the experimental objects. The hybrid model was used to identify and switch for the most appropriate soft contact model when dealing with a certain type of animal tissue. The performance of the hybrid model on force estimation was compared with several individual soft contact models. The results showed that the estimated/reconstructed force of the hybrid model was closer to the ground truth measured by the force sensor. In addition, a new reference soft contact model has been purposely added online to verify the expandability of the hybrid model.

Grasping of objects using multi-fingered robotic hands often fails due to small uncertainties in the hand motion control and the object's pose estimation. To tackle this problem, we propose a grasping adjustment strategy based on tactile seroving. Our technique employs feedback from a sensorized multi-fingered robotic hand to collaboratively servo the fingers and palm to achieve the desired grasp. We demonstrate the performance of our method through simulation and physical experiments by having a robot grasp different objects under conditions of variable uncertainty. The results show that our approach achieved a higher success rate and tolerated greater uncertainty than an open-looped grasp.