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.

For a robot with redundant sensors and actuators distributed throughout its body, it is difficult to construct a controller or a neural network using all of them due to computational cost and complexity. Therefore, it is effective to extract functionally related sensors and actuators, group them, and construct a controller or a network for each of these groups. In this study, the functional and spatial connections among sensors and actuators are embedded into a graph structure and a method for automatic grouping is developed. Taking a musculoskeletal humanoid with a large number of redundant muscles as an example, this method automatically divides all the muscles into regions such as the forearm, upper arm, scapula, neck, etc., which has been done by humans based on a geometric model. The functional relationship among the muscles and the spatial relationship of the neural connections are calculated without a geometric model.

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.

Forearm of Kengoro, composed of newly developed miniature bone-muscle module. In recent years, development of the humanoid is vigorous. The humanoid, beginning with the ASIMO [1], has two arms The conventional method of installing the muscle modules and two legs, and can move and walk like a human. The such as [8], [9] to the structure excels in maintainability and development of not only the humanoid, but of the tendondriven reliability, and includes electric motors, which have better musculoskeletal humanoid, which is based on various controllability. However, we need to miniaturize the modules parts of the human body, is also vigorous [2], [3]. The or propose other approaches in order to achieve many DOFs tendon-driven musculoskeletal humanoid is based on not without deviating from the human body proportion, because only the body proportion but also the joint structure, drive the conventional muscle modules are large in size, and system, and muscle arrangement of the human body, and is need other wasteful structures to function. Additionally, the used to analyze human motion and to achieve human skillful muscle arrangements, the proportion of the forearm, and the motion. Of these studies, there are many which duplicate the benefits of the radioulnar structure are not discussed at all human joint structure.

Tendon-driven musculoskeletal humanoids typically have complex structures similar to those of human beings, such as ball joints and the scapula, in which encoders cannot be installed. Therefore, joint angles cannot be directly obtained and need to be estimated using the changes in muscle lengths. In previous studies, methods using table-search and extended kalman filter have been developed. These methods express the joint-muscle mapping, which is the nonlinear relationship between joint angles and muscle lengths, by using a data table, polynomials, or a neural network. However, due to computational complexity, these methods cannot consider the effects of polyarticular muscles. In this study, considering the limitation of the computational cost, we reduce unnecessary degrees of freedom, divide joints and muscles into several groups, and formulate a joint angle estimation method that takes into account polyarticular muscles. Also, we extend the estimation method to propose a joint angle estimation method using only the relative changes in muscle lengths. By this extension, which does not use absolute muscle lengths, we do not need to execute a difficult calibration of muscle lengths for tendon-driven musculoskeletal humanoids. Finally, we conduct experiments in simulation and actual environments, and verify the effectiveness of this study.

The body structures of tendon-driven musculoskeletal humanoids are complex, and accurate modeling is difficult, because they are made by imitating the body structures of human beings. For this reason, we have not been able to move them accurately like ordinary humanoids driven by actuators in each axis, and large internal muscle tension and slack of tendon wires have emerged by the model error between its geometric model and the actual robot. Therefore, we construct a joint-muscle mapping (JMM) using a neural network (NN), which expresses a nonlinear relationship between joint angles and muscle lengths, and aim to move tendon-driven musculoskeletal humanoids accurately by updating the JMM online from data of the actual robot. In this study, the JMM is updated online by using the vision of the robot so that it moves to the correct position (Vision Updater). Also, we execute another update to modify muscle antagonisms correctly (Antagonism Updater). By using these two updaters, the error between the target and actual joint angles decrease to about 40% in 5 minutes, and we show through a manipulation experiment that the tendon-driven musculoskeletal humanoid Kengoro becomes able to move as intended. This novel system can adapt to the state change and growth of robots, because it updates the JMM online successively.

Human can not only support their body during standing or walking, but also support them by hand, so that they can dangle a bar and others. But most humanoid robots support their body only in the foot and they use their hand just to manipulate objects because their hands are too weak to support their body. Strong hands are supposed to enable humanoid robots to act in much broader scene. Therefore, we developed new life-size five-fingered hand that can support the body of life-size humanoid robot. It is tendon-driven and underactuated hand and actuators in forearms produce large gripping force. This hand has flexible joints using machined springs, which can be designed integrally with the attachment. Thus, it has both structural strength and impact resistance in spite of small size. As other characteristics, this hand has force sensors to measure external force and the fingers can be flexed along objects though the number of actuators to flex fingers is less than that of fingers. We installed the developed hand on musculoskeletal humanoid "Kengoro" and achieved two self-weight supporting motions: push-up motion and dangling motion.

The robotics group at the Institute for Human & Machine Cognition (IHMC) in Pensacola, Fla., has an enormous amount of experience with walking robots. They came in second at the DARPA Robotics Challenge with their Running Man Atlas, one of just three teams to score a perfect 8 out of 8, and they've continued to advance bipedal locomotion using both Atlas and NASA's Valkyrie. We write about their research all the time--just a few months ago, they taught Atlas to walk with straight legs, much like a human does. Humans set a very high standard for bipedal mobility. We're well designed for it in both hardware and software, and we can do some absolutely amazing things.

A team of researchers at the University of Tokyo developed a pair of humanoid robots that can carry out a wide range of life-like activities -- such as doing push ups, sit ups, and stretches as well as playing badminton and other complicated motions -- in a less mechanical manner compared with most automatons. The robots, called Kengoro and Kenshiro, were designed in such a way that mimics the human body's muscular and skeletal systems. Kenshiro was developed between 2011 and 2014, while Kengoro was developed from 2015 onward. "For at least the last two millennia, human beings have endeavored to understand the systems and mechanisms that make up the human body. However, a limitation of conventional humanoids is that they have been designed on the basis of the theories of conventional engineering, mechanics, electronics, and informatics," lead researcher Yuki Asano told Daily Mail online. The scientists used aluminum, steel, and plastic as a frame for the humanoid robots.

A team of researchers at the University of Tokyo developed a pair of humanoid robots that can carry out a wide range of life-like activities -- such as doing push ups, sit ups, and stretches as well as playing badminton and other complicated motions -- in a less mechanical manner compared with most automatons. The robots, called Kengoro and Kenshiro, were designed in such a way that mimics the human body's muscular and skeletal systems. Kenshiro was developed between 2011 and 2014, while Kengoro was developed from 2015 onward. "For at least the last two millennia, human beings have endeavored to understand the systems and mechanisms that make up the human body. However, a limitation of conventional humanoids is that they have been designed on the basis of the theories of conventional engineering, mechanics, electronics, and informatics," lead researcher Yuki Asano told Daily Mail online. The scientists used aluminum, steel, and plastic as a frame for the humanoid robots.