-- Robot hands that imitate the shape of the human body have been actively studied, and various materials and mechanisms have been proposed to imitate the human body. Although the use of soft materials is advantageous in that it can imitate the characteristics of the human body's epidermis, it increases the number of parts and makes assembly di fficult in order to perform complex movements. In this study, we propose a skin-skeleton integrated robot hand that has 15 degrees of freedom and consists of four parts. The developed robotic hand is mostly composed of a single flexible part produced by a 3D printer, and while it can be easily assembled, it can perform adduction, flexion, and opposition of the thumb, as well as flexion of four fingers. I ntroduction Robots are being used to automate tasks previously performed by humans, with robot hands playing a particularly important role. In a social implementation, changing hands according to the task is problematic in terms of implementation cost. However, a robot hand that can perform many tasks with a single hand has advantages such as greatly reducing the cost of introduction and contributing greatly to the realization of an automated society. Most tools in society are made to fit human hands, so the human mimetic robot hand can be implemented in society without the use of special tools.

-- 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.

The musculoskeletal humanoid has various biomimetic benefits, and it is important that we can embed and evaluate human reflexes in the actual robot. Although stretch reflex has been implemented in lower limbs of musculoskeletal humanoids, we apply it to the upper limb to discover its useful applications. We consider the implementation of stretch reflex in the actual robot, its active/passive applications, and the change in behavior according to the difference of parameters.

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.

The musculoskeletal humanoid has various biomimetic benefits, and the redundant muscle arrangement is one of its most important characteristics. This redundancy can achieve fail-safe redundant actuation and variable stiffness control. However, there is a problem that the maximum joint angle velocity is limited by the slowest muscle among the redundant muscles. In this study, we propose two methods that can exceed the limited maximum joint angle velocity, and verify the effectiveness with actual robot experiments.

In this study, we propose a predictive model composed of a recurrent neural network including parametric bias and stochastic elements, and an environmentally adaptive robot control method including variance minimization using the model. Robots which have flexible bodies or whose states can only be partially observed are difficult to modelize, and their predictive models often have stochastic behaviors. In addition, the physical state of the robot and the surrounding environment change sequentially, and so the predictive model can change online. Therefore, in this study, we construct a learning-based stochastic predictive model implemented in a neural network embedded with such information from the experience of the robot, and develop a control method for the robot to avoid unstable motion with large variance while adapting to the current environment. This method is verified through a mobile robot in simulation and to the actual robot Fetch.

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.

Abstract-- The purpose of this study is to construct a contact point estimation system for the both side of a finger, and to realize a motion of bending the finger after inserting the finger into a tool (hereinafter referred to as the bending after insertion motion). In order to know the contact points of the full finger including the joints, we propose to fabricate a nerve inclusion flexible epidermis by combining a flexible epidermis and a nerve line made of conductive filaments, and estimate the contact position from the change of resistance of the nerve line. A nerve inclusion flexible epidermis attached to a thin fingered robotic hand was combined with a twin-armed robot and tool use experiments were conducted. The contact information can be used for tool use, confirming the effectiveness of the proposed method. I. Introduction A. Outline of the Bending after Insertion Motion degree of freedom to grasp and use scissors.