Musculoskeletal AutoEncoder: A Unified Online Acquisition Method of Intersensory Networks for State Estimation, Control, and Simulation of Musculoskeletal Humanoids

Abstract

While the musculoskeletal humanoid has various biomimetic benefits, the modeling of its complex structure is difficult, and many learning-based systems have been developed so far. There are various methods, such as control methods using acquired relationships between joints and muscles represented by a data table or neural network, and state estimation methods using Extended Kalman Filter or table search. In this study, we construct a Musculoskeletal AutoEncoder representing the relationship among joint angles, muscle tensions, and muscle lengths, and propose a unified method of state estimation, control, and simulation of musculoskeletal humanoids using it. By updating the Musculoskeletal AutoEncoder online using the actual robot sensor information, we can continuously conduct more accurate state estimation, control, and simulation than before the online learning. We conducted several experiments using the musculoskeletal humanoid Musashi, and verified the effectiveness of this study.

Accepted at IEEE Robotics and Automation Letters

References in corpus (4)

• Component Modularized Design of Musculoskeletal Humanoid Platform Musashi to Investigate Learning Control Systems
• Long-time Self-body Image Acquisition and its Application to the Control of Musculoskeletal Structures
• Online Learning of Joint-Muscle Mapping Using Vision in Tendon-driven Musculoskeletal Humanoids
• Online Self-body Image Acquisition Considering Changes in Muscle Routes Caused by Softness of Body Tissue for Tendon-driven Musculoskeletal Humanoids

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• Online Learning of Danger Avoidance for Complex Structures of Musculoskeletal Humanoids and Its Applications
• Automatic Grouping of Redundant Sensors and Actuators Using Functional and Spatial Connections: Application to Muscle Grouping for Musculoskeletal Humanoids
• Realization of Seated Walk by a Musculoskeletal Humanoid with Buttock-Contact Sensors From Human Constrained Teaching
• Characteristics, Management, and Utilization of Muscles in Musculoskeletal Humanoids: Empirical Study on Kengoro and Musashi
• GeMuCo: Generalized Multisensory Correlational Model for Body Schema Learning
• Motion Modification Method of Musculoskeletal Humanoids by Human Teaching Using Muscle-Based Compensation Control
• PIMBS: Efficient Body Schema Learning for Musculoskeletal Humanoids with Physics-Informed Neural Networks