Learning Conditional Postural Synergies for Dexterous Hands: A Generative Approach Based on Variational Auto-Encoders and Conditioned on Object Size and Category

Abstract

Postural synergies are used in robotics to facilitate the control of dexterous artificial hands. This is achieved by learning a latent space (synergy space) from grasp postures and directly controlling the hand in this space. In this work, we propose the use of a non-linear conditional model for learning the latent space, that can incorporate the object shape and size as additional variables. While on most of the previous works the evaluation criterion is the reconstruction error, we propose to use the smoothness of the latent space. Our model ranks better than other non-linear models in smoothness, which is a better criterion to evaluate in-hand manipulation tasks. We validate our arguments by executing regrasp trajectories in which our model outperforms all previous approaches.