I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

Many systems and mechanisms in the human body are not fully understood, such as the principles of muscle control, the sensory nervous system that connects the brain and the body, learning in the brain, and the human walking motion. To address this knowledge deficit, we propose a human mimetic humanoid with an unprecedented degree of anatomical fidelity to the human musculoskeletal structure. The fundamental concept underlying our design is to consider the human mechanism, which contrasts with the conventional engineering approach used in the design of existing humanoids. We believe that the proposed human mimetic humanoid can be used to provide new opportunities in science, for instance, to quantitatively analyze the internal data of a human body in movement. We describe the principles and development of human mimetic humanoids, Kenshiro and Kengoro, and compare their anatomical fidelity with humans in terms of body proportions, skeletal structures, muscle arrangement, and joint performance. To demonstrate the potential of human mimetic humanoids, Kenshiro and Kengoro performed several typical human motions.

Design principles of a human mimetic humanoid: Humanoid platform to study human intelligence and internal body system

Anthropomimetic robotics differs from conventional approaches by capitalizing on the replication of the inner structures of the human body, such as muscles, tendons, bones, and joints. Here we present our results of more than three years of research in constructing, simulating, and, most importantly, controlling anthropomimetic robots. We manufactured four physical torsos, each more complex than its predecessor, and developed the tools required to simulate their behavior. Furthermore, six different control approaches, inspired by classical control theory, machine learning, and neuroscience, were developed and evaluated via these simulations or in small-scale setups. While the obtained results are encouraging, we are aware that we have barely exploited the potential of the anthropomimetic design so far. But, with the tools developed, we are confident that this novel approach will contribute to our understanding of morphological computation and human motor control in the future.

/pdf/toward-anthropomimetic-robotics-development-simulation-and-ysuv97cfqs.pdf

Toward anthropomimetic robotics: Development, simulation, and control of a musculoskeletal torso

In order to know human dynamics, humanoid as a human body simulator is increasing its importance. Such humanoid is expected to have human musculoskeletal structure as close as possible. From this viewpoint, we are trying to create new musculoskeletal humanoid which has detailed human imitating structure, such as bi-articular muscle, muscle arrangement, joint structure and so on. In this paper, we address the design of lower thigh. The concepts of the thigh include leg configuration, new knee joint and link, and artificial muscle arrangements, especially knee joint structure imitating human flexible motion. The knee joint has yaw axis DOF and its locking mechanism which is usually simplified in robotics. Finally, we conduct extension, flexion and rotation as basic experiment to confirm the joint characteristics. Also, we conduct rotation experiment in the ground state to confirm the contribution of yaw axis DOF for human-like motion.

Lower thigh design of detailed musculoskeletal humanoid “Kenshiro”

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 min and doing pull-ups.

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

In the long history of robotics research, the most prominent problem has always been, to develop robots that can safely operate in human-centered environments. One way towards the goal of a safe, and human-friendly robot, is to incorporate more and more of the flexibility that can be found in humans, by mimicking the internal mechanisms. In this work we propose a scalable joint-space control scheme based on computed torque control for an anthropomimetic robot. To achieve this, the dynamic system model of the robot is decomposed into hierarchical subsystems, using scalable modeling algorithms where possible. Machine learning techniques were employed to tackle the problem of muscle force to joint torque mapping. The developed control scheme has been evaluated using the highly refined simulation of an anthropomimetic robot arm featuring 11 muscles, a revolute elbow joint and a spherical shoulder joint. We show trajectory tracking based on a low-level muscle and a high-level joint control scheme, taking into account the coupling between the joints due to inertial reactions and bi-articular muscles.

/pdf/a-scalable-joint-space-controller-for-musculoskeletal-robots-rup5eiylvq.pdf

Christian Schmaler

Papers

A scalable joint-space controller for musculoskeletal robots with spherical joints