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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