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A version of this paper with color figures is available online at http://dx.doi.org/10.1162/ artl_a_00088. Subscription required.Abstract 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.

Abstract-Anthropomimetic robotics differ from conventional approaches by capitalizing on the replication of the inner structures of the human body, such as muscles, tendons, bones and joints [1]. Prominent examples for this class of robots are the robots developed at the JSK laboratory of the University of Tokyo and the robots developed by the EU-funded project Embodied Cognition in a Compliantly Engineered Robot (Eccerobot). However, the high complexity of these robots as well as their lack of sensors has so far failed to provide the desired new insights in the field of control.Therefore, we developed the simplified but sensorized robot Anthrob. The robot replicates the human upper limb and features 13 compliant tendon driven uni-and biarticular muscles as well as a spherical shoulder joint. Whenever possible, Selective Laser Sintering (SLS) was used for the production of the robot parts to reduce the production costs and to implement cutting-edge technologies, such as tendon canals or solid-state joints.

Abstract-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 lowlevel muscle and a high-level joint control scheme, taking into account the coupling between the joints due to inertial reactions and bi-articular muscles.

Abstract-The control of tendon-driven robots using techniques from traditional robotics remains a very challenging task that has been so far only successfully achieved for small-scale setups comprising exclusively revolute joints

Abstract-The soft robotics approach is widely considered to enable human-friendly robots which are able to work in our future homes and factories. Furthermore, achieving the smooth and natural movements of humans has become a hot topic in robotics, especially when robots are supposed to work in close proximity to humans. The anthropomimetic principle aims at mimicking not only the outside but also the inner mechanisms of the human body in humanoid robots. However, for this class of robots there exist as yet no scalable controllers that might make it possible to control a full body, or even several joints. A very similar problem is ongoing research in biomechanics which is the computation of muscle excitation patterns for coordinated movements. For this purpose, biomechanicists have developed computed muscle control which has proven a very scalable technique.In this paper, we demonstrate the adaptation of computed muscle control for a tendon-driven robot, comparing different methods for obtaining the muscle kinematics, as well as different low-level controllers. Results are shown for the implementation on a distributed control architecture and a single revolute elbow joint.

Abstract-Major progress in robotics turns today's humanoid robots into ever safer, more robust, and more agile agents by the moment. However, it is still a long way until robots can safely operate in open environments. Especially in the area of service robotics, the need arises for robots to work flexibly in a human centered environment. One way towards this goal is to incorporate more and more of the mechanisms that can be found in humans for our robots. In this work we would like to propose a bio-inspired control architecture for an equally bioinspired -namely anthropomimetic -humanoid robot. To achieve this, the human motor control system is analyzed and copied at a structural level. This results in a distributed control infrastructure that is capable of reducing the complexity of the control task by off-loading parts of the control problem into the robot's limbs. Finally, we will prove the fact that it is possible to control an anthropomimetic robot with a large number of degrees of freedom with the proposed control architecture.

Abstract-The control of tendon-driven and, in particular, of anthropomimetic robots using techniques from traditional robotics remains a very challenging task [1,2]. Hence, we previously proposed to employ physics-based simulation engines to simulate the complex dynamics of this emerging class of robots [3] and to use the simulation model as an internal model for robot control [4]. This approach, however, relies on an accurate model to be successful.In this paper, we present the automated, steady-state pose calibration of a physics-based, anthropomimetic robot model using a (µ, λ)-Evolution Strategy. For the acquisition of the poses of the physical robot, a stereo-vision, infrared-marker based motion capture system with real-time capabilities was developed. The employed (µ, λ)-Evolution Strategy uses a Gaussian-based, non-isotropic, self-adapting mutation operator to explore the search space and reduce the simulation-reality gap. The obtained results are impressive, resulting in a reduction of joint angle errors in the range of one to two orders of magnitude and an absolute joint angle error of 0.5• -4.5• per pose evaluated.