Sequential recognition of in-hand object shape using a collection of neural forests

Abstract

Tactile object shape identification is important for robotic hands to perform dexterous manipulation. Most of the proposed approaches concentrate on specific object recognition. This limits their application into more realistic environments where a larger amount of objects are present. We present a method that performs object shape identification independently on the size and location of the object within the hand. This method allows sequential learning of new shapes. The method combines proprioceptive signatures and contact normal information to build a descriptor that reduces the impact of the size and pose of the object on recognition rate. Sequential training is performed with a collection of Neural Forests (NF). This allows adding new objects to the training set so that training the model from scratch is avoided. Extensive experiments reveal that the combination of multiple modalities (e.g. contact normals, proprioceptive information) is beneficial to the system accuracy, and that results for sequential learning are on par with its batch counterpart. This makes sequential training advantageous because it is less time consuming. Results showed that both techniques depict similar results and perform with at least 83% in a 7-shapes case scenario in a simulated environment. Experiments are made with a real shadow hand using a NF trained on simulated data.