CatBoost for Haptic Modeling of Homogeneous Viscoelastic Deformable Objects

This paper proposes an alternative data-driven haptic modeling method of homogeneous deformable objects based on a CatBoost approach-a variant of gradient boosting machine learning approach. In this approach, decision trees are trained sequentially to learn the required mapping function for modeling the objects. The model is trained on the input feature vectors consisting of position, velocity and filtered velocity samples to estimate the response force. Our approach is validated with a publicly available two-finger grasping dataset. The proposed approach can model unknown interactions with good accuracy (relative root mean squared error, absolute relative error and maximum error less than 0.06, 0.18 and 0.76 N, respectively) when trained on just 20% of the training data. The CatBoost-based method outperforms the existing data-driven methods both in terms of the prediction accuracy and the modeling time when trained on similar size of the training data.