Iterative Learning Scheme for Dexterous In-hand Manipulation with Stochastic Uncertainty

In-hand manipulation has attracted attention because of its potential for performing dexterous manipulation tasks. Few successful examples using real robotic fingers have been reported because model-based approaches have been assumed. A gradient descent-based iterative learning control is one of the typical methods for improving the control performance without the need for a precise model. However, the learning performances deteriorate greatly owing to the stochastic uncertainties, and the learning rates have to be determined manually. We propose a novel iterative learning scheme with adaptive learning rate methods for dexterous inhand manipulation. The proposed scheme not only eliminates the need for a precise model and manual tuning of a learning rate but also is robust to stochastic uncertainties and insensitive to hyperparameters. The validity of the proposed iterative learning scheme is demonstrated through several experiments.