Dynamic motion learning for multi-DOF flexible-joint robots using active–passive motor babbling through deep learning. (17th September 2017)

• Record Type: Journal Article
• Authors: Takahashi, Kuniyuki; Ogata, Tetsuya; Nakanishi, Jun; Cheng, Gordon; Sugano, Shigeki
• Is Part Of: Advanced robotics. Volume 31:Number 18(2017)
• Page Start: 1002
• Page End: 1015
• Publication Date: 2017-09-17
• Subjects: Motor babbling -- flexible-joint robot -- dynamic motion learning -- recurrent neural network -- deep learning; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2017.1383939
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : This paper proposes a learning strategy for robots with flexible joints having multi-degrees of freedom in order to achieve dynamic motion tasks. In spite of there being several potential benefits of flexible-joint robots such as exploitation of intrinsic dynamics and passive adaptation to environmental changes with mechanical compliance, controlling such robots is challenging because of increased complexity of their dynamics. To achieve dynamic movements, we introduce a two-phase learning framework of the body dynamics of the robot using a recurrent neural network motivated by a deep learning strategy. The proposed methodology comprises a pre-training phase with motor babbling and a fine-tuning phase with additional learning of the target tasks. In the pre-training phase, we consider active and passive exploratory motions for efficient acquisition of body dynamics. In the fine-tuning phase, the learned body dynamics are adjusted for specific tasks. We demonstrate the effectiveness of the proposed methodology in achieving dynamic tasks involving constrained movement requiring interactions with the environment on a simulated robot model and an actual PR2 robot both of which have a compliantly actuated seven degree-of-freedom arm. The results illustrate a reduction in the required number of training iterations for task learning and generalization capabilities for untrained situations. Abstract : The proposed learning framework for acquiring body dynamics in two phases (pre-training and fine-tuning). In the pre-training phase, the robot acquires body dynamics with an RNN through motor babbling. We consider a sequence of active and passive motions to improve the efficiency in the learning process of the body dynamics. Then, in the fine-tuning phase, the robot performs additional learning to adjust acquired body dynamics to the target task. The objective of this strategy is to efficiently learn the desired movements to perform the given tasks with the reduction of training iterations and generalization to untrained situations with the learned body dynamics. The below listed points should be captured along with the Graphical abstract image Dynamic motion tasks for robots with flexible joints having multi-DOFs Pre-training with motor babbling and fine-tuning with additional learning Active and passive exploratory motions in motor babbling.