Task-specific Self-body Controller Acquisition by Musculoskeletal Humanoids: Application to Pedal Control in Autonomous Driving

Abstract

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 self-body 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 time-series relationship between the control input and task state, and realize the intended task state by applying the network to a real-time control. In this research, we conduct accelerator pedal control experiments as one application, and verify the effectiveness of this study.

Accepted at IROS2019

References in corpus (8)

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Cited by in corpus (5)

• Toward Autonomous Driving by Musculoskeletal Humanoids: A Study of Developed Hardware and Learning-Based Software
• Deep Predictive Model Learning with Parametric Bias: Handling Modeling Difficulties and Temporal Model Changes
• Development of Musculoskeletal Legs with Planar Interskeletal Structures to Realize Human Comparable Moving Function
• Stable Tool-Use with Flexible Musculoskeletal Hands by Learning the Predictive Model of Sensor State Transition
• Motion Modification Method of Musculoskeletal Humanoids by Human Teaching Using Muscle-Based Compensation Control