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

TL;DR

This paper introduces a Musculoskeletal AutoEncoder that enables unified online state estimation, control, and simulation of musculoskeletal humanoids, improving accuracy through real-time sensor data updates.

Contribution

It proposes a novel autoencoder model that integrates musculoskeletal relationships and enables online learning for enhanced humanoid control and simulation.

Findings

Improved accuracy in state estimation through online updates.

Effective control and simulation demonstrated on Musashi humanoid.

Unified framework reduces complexity of musculoskeletal modeling.

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.