Taskspecific Selfbody Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving The musculoskeletal humanoid has many benefits that human beings have, but the modeling of its complex flexible body is difficult. Although we have developed an online acquisition method of the nonlinear relationship between joints and muscles, we could not completely match the actual robot and its selfbody image. When realizing a certain task, the direct relationship between the control input and task state needs to be learned. So, we construct a neural network representing the timeseries relationship between the control input and task state, and realize the intended task state by applying the network to a realtime control. In this research, we conduct accelerator pedal control experiments as one application, and verify the effectiveness of this study.

Dynamic Task Control Method of a Flexible Manipulator Using a Deep Recurrent Neural Network The flexible body has advantages over the rigid body in terms of environmental contact thanks to its underactuation. On the other hand, when applying conventional control methods to realize dynamic tasks with the flexible body, there are two difficulties: accurate modeling of the flexible body and the derivation of intermediate postures to achieve the tasks. Learningbased methods are considered to be more effective than accurate modeling, but they require explicit intermediate postures. To solve these two difficulties at the same time, we developed a realtime task control method with a deep recurrent neural network named Dynamic Task Execution Network (DTXNET), which acquires the relationship among the control command, robot state including image information, and task state. Once the network is trained, only the target event and its timing are needed to realize a given task. To demonstrate the effectiveness of our method, we applied it to the task of Wadaiko (traditional Japanese drum) drumming as an example, and verified the best configuration of DTXNET.