-- Although robots with flexible bodies are superior in terms of the contact and adaptability, it is difficult to control them precisely. On the other hand, human beings make use of the surrounding environments to stabilize their bodies and control their movements. In this study, we propose a method for the bracing motion and extension of the range of motion using the environment for the musculoskeletal humanoid. Here, it is necessary to recognize the stability of the body when contacting the environment, and we develop a method to measure it by using the change in sensor values of the body when actively vibrating a part of the body. Experiments are conducted using the musculoskeletal humanoid Musashi, and the effectiveness of this method is confirmed. I. INTRODUCTION The flexible body is excellent from the point of view of the soft contact, impact mitigation, adaptability, etc. [1], [2], and a shift from rigid robots [3], [4] to soft robots [5], [6] is underway. In [5], a robot that jumps and runs using pneumatic artificial muscles is developed. In [6], a robot that mitigates impact and softly interacts with the environment using variable stiffness control with nonlinear elastic elements has been developed.

-- The complex structure of musculoskeletal humanoids makes it difficult to model them, and the inter-body interference and high internal muscle force are unavoidable. Although various safety mechanisms have been developed to solve this problem, it is important not only to deal with the dangers when they occur but also to prevent them from happening. In this study, we propose a method to learn a network outputting danger probability corresponding to the muscle length online so that the robot can gradually prevent dangers from occurring. Applications of this network for control are also described. The method is applied to the musculoskeletal humanoid, Musashi, and its effectiveness is verified. I. INTRODUCTION The musculoskeletal humanoid [1]-[4] has various biomimetic advantages such as variable stiffness using redundant muscles, spherical joints without singular points, underactuated and flexible fingers, etc. At the same time, its complex musculoskeletal structure is difficult to model and various learning control methods have been developed [5]- [8].

-- The musculoskeletal humanoid can move well under environmental contact thanks to its body softness. However, there are few studies that actively make use of the environment to rest its flexible musculoskeletal body. Also, its complex musculoskeletal structure is difficult to modelize and high internal muscle tension sometimes occurs. T o solve these problems, we develop a muscle relaxation control which can minimize the muscle tension by actively using the environment and inhibit useless internal muscle tension. We apply this control to some basic movements, the motion of resting the arms on the desk, and handle operation, and verify its effectiveness. I. INTRODUCTION The musculoskeletal humanoid [1]-[4] has many biomimetic benefits such as muscle redundancy, variable stiffness control using nonlinear elastic elements, ball joints without extreme points, and the under-actuated fingers and spine.

Musculoskeletal humanoids have various biomimetic advantages, and the redundant muscle arrangement allowing for variable stiffness control is one of the most important. In this study, we focus on one feature of the redundancy, which enables the humanoid to keep moving even if one of its muscles breaks, an advantage that has not been dealt with in many studies. In order to make the most of this advantage, the design of muscle arrangement is optimized by considering the maximization of minimum available torque that can be exerted when one muscle breaks. This method is applied to the elbow of a musculoskeletal humanoid Musashi with simulations, the design policy is extracted from the optimization results, and its effectiveness is confirmed with the actual robot.

In recent years, some research on musculoskeletal humanoids is in progress. However, there are some challenges such as unmeasurable transformation of body structure and muscle path, and difficulty in measuring own motion because of lack of joint angle sensor. In this study, we suggest two motion acquisition methods. One is a method to acquire antagonistic relations of muscles by tension sensing, and the other is a method to acquire correct hand trajectory by vision sensing. Finally, we realize badminton shuttlecock-hitting motion of Kengoro with these two acquisition methods.

Abstract-- While musculoskeletal humanoids have the advantages of various biomimetic structures, it is difficult to accurately control the body, which is challenging to model. Although various learning-based control methods have been developed so far, they cannot completely absorb model errors, and recognition errors are also bound to occur. In this paper, we describe a method to modify the movement of the musculoskeletal humanoid by applying external force during the movement, taking advantage of its flexible body. Considering the fact that the joint angles cannot be measured, and that the external force greatly affects the nonlinear elastic element and not the actuator, the modified motion is reproduced by the proposed muscle-based compensation control. This method is applied to a musculoskeletal humanoid, Musashi, and its effectiveness is confirmed.

We proposed a method for learning the actual body image of a musculoskeletal humanoid for posture generation and object manipulation using inverse kinematics with redundancy in the shoulder complex. The effectiveness of this method was confirmed by realizing automobile steering wheel operation. The shoulder complex has a scapula that glides over the rib cage and an open spherical joint, and is supported by numerous muscle groups, enabling a wide range of motion. As a development of the human mimetic shoulder complex, we have increased the muscle redundancy by implementing deep muscles and stabilize the joint drive. As a posture generation method to utilize the joint redundancy of the shoulder complex, we consider inverse kinematics based on the scapular drive strategy suggested by the scapulohumeral rhythm of the human body. In order to control a complex robot imitating a human body, it is essential to learn its own body image, but it is difficult to know its own state accurately due to its deformation which is difficult to measure. To solve this problem, we developed a method to acquire a self-body image that can be updated appropriately by recognizing the hand position relative to an object for the purpose of object manipulation. We apply the above methods to a full-body musculoskeletal humanoid, Kengoro, and confirm its effectiveness by conducting an experiment to operate a car steering wheel, which requires the appropriate use of both arms.

Musculoskeletal humanoids have various biomimetic advantages, of which redundant muscle arrangement is one of the most important features. This feature enables variable stiffness control and allows the robot to keep moving its joints even if one of the redundant muscles breaks, but this has been rarely explored. In this study, we construct a neural network that represents the relationship among sensors in the flexible and difficult-to-modelize body of the musculoskeletal humanoid, and by learning this neural network, accurate motions can be achieved. In order to take advantage of the redundancy of muscles, we discuss the use of this network for muscle rupture detection, online update of the intersensory relationship considering the muscle rupture, and body control and state estimation using the muscle rupture information. This study explains a method of constructing a musculoskeletal humanoid that continues to move and perform tasks robustly even when one muscle breaks.

This paper summarizes an autonomous driving project by musculoskeletal humanoids. The musculoskeletal humanoid, which mimics the human body in detail, has redundant sensors and a flexible body structure. These characteristics are suitable for motions with complex environmental contact, and the robot is expected to sit down on the car seat, step on the acceleration and brake pedals, and operate the steering wheel by both arms. We reconsider the developed hardware and software of the musculoskeletal humanoid Musashi in the context of autonomous driving. The respective components of autonomous driving are conducted using the benefits of the hardware and software. Finally, Musashi succeeded in the pedal and steering wheel operations with recognition.

Abstract-- Musculoskeletal humanoids have been developed by imitating humans and expected to perform natural and dynamic motions as well as humans. To achieve desired motions stably in current musculoskeletal humanoids is not easy because they cannot maintain the sufficient moment arm of muscles in various postures. In this research, we discuss planar structures that spread across joint structures such as ligament and planar muscles and the application of planar interskeletal structures to humanoid robots. Next, we develop MusashiOLegs, a musculoskeletal legs which has planar interskeletal structures and conducts several experiments to verify the importance of planar interskeletal structures. I. INTRODUCTION The tendon-driven musculoskeletal humanoids [1], [2] which imitates a human body structure, have muscles around Muskuloskeletal humanoids are designed as a model of human beings and expected to perform as a rigid mechanism. These rigid joints restriction cannot natural and dynamic motions as a human being can do.