Uploaded May 2018 | Updated September 2026, 2 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Wed AM Pod I.5
Authors: Yashima, Masahito; Yamawaki, Tasuku
Title: Iterative Learning Scheme for Dexterous In-Hand Manipulation with Stochastic Uncertainty
Abstract:
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 in-hand 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.
ICRA 2018 Spotlight Video
Interactive Session Wed AM Pod I.5
Authors: Yashima, Masahito; Yamawaki, Tasuku
Title: Iterative Learning Scheme for Dexterous In-Hand Manipulation with Stochastic Uncertainty
Abstract:
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 in-hand 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.
