Antagonist Inhibition Control in Redundant Tendon-driven Structures Based on Human Reciprocal Innervation for Wide Range Limb Motion of Musculoskeletal Humanoids

TL;DR

This paper introduces an antagonist inhibition control method inspired by human reciprocal innervation to enhance safe, wide-range limb motion in tendon-driven humanoids, effectively compensating for model inaccuracies.

Contribution

The study presents a novel AIC approach based on reflexes, enabling safe, long-duration motions in tendon-driven humanoids despite geometric modeling errors.

Findings

Achieved 14-minute dangling without issues

Successfully performed pull-ups with the humanoid

Demonstrated effective compensation for model inaccuracies

Abstract

The body structure of an anatomically correct tendon-driven musculoskeletal humanoid is complex, and the difference between its geometric model and the actual robot is very large because expressing the complex routes of tendon wires in a geometric model is very difficult. If we move a tendon-driven musculoskeletal humanoid by the tendon wire lengths of the geometric model, unintended muscle tension and slack will emerge. In some cases, this can lead to the wreckage of the actual robot. To solve this problem, we focused on reciprocal innervation in the human nervous system, and then implemented antagonist inhibition control (AIC) based on the reflex. This control makes it possible to avoid unnecessary internal muscle tension and slack of tendon wires caused by model error, and to perform wide range motion safely for a long time. To verify its effectiveness, we applied AIC to the upper limb of the tendon-driven musculoskeletal humanoid, Kengoro, and succeeded in dangling for 14 minutes and doing pull-ups.