The soft robotics approach is widely considered to enable robots in the near future to leave their cages and move freely in our modern homes and manufacturing sites. Musculoskeletal robots are such soft robots which feature passively compliant actuation, while leveraging the advantages of tendon-driven systems. Even though these robots have been intensively researched within the last decade, high-performance feedback control laws have only very recently been developed. In [1], a controller was developed utilizing Dynamic Surface Control (DSC), an extension to backstepping, with an adaptive neural network compensator for joint as well as muscle friction. We compare these novel control strategies to Computed Force Control (CFC), an existing technique from the field of tendon-driven control, yielding highly improved trajectory tracking. The musculoskeletal robot Anthrob [2] serves as a benchmark.

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.

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.

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.

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 [1, 2]. Hence, we propose a fundamentally different approach. Instead of deriving an analytical robot model using either the Newton-Euler or Lagrangian formulation we suggest to employ physics-based simulation engines to simulate the peculiar dynamics of this emerging class of robots and to use the simulated robot model as an internal model for robot control [3]. In this paper, we present the reverse-engineered derivation of a detailed physics-based model of an anthropomimetic robot implemented on CALIPER [4], a simulation framework developed within the EU-funded project ECCEROBOT [5]. The model comprises an accurate model of the skeleton derived from laser scan data, as well as of artificial ligaments and muscles. The individual sub-models are validated separately against measurements and the successful integration of all sub-models is demonstrated by executing a limb movement which requires the parallel control of multiple muscles.

The development of increasingly complex robots in recent years has been characterized by an extensive use of physics-based simulations for controller design and optimization. Today, a variety of open-source and commercial simulators exist for this purpose for mobile and industrial robots. However, existing simulation engines still lack support for the emerging class of tendon-driven robots. In this paper, an innovative simulation framework for the simulation of tendon-driven robots is presented. It consists of a generic physics simulator capable of utilizing CAD robot models and a set of additional tools for simulation control, data acquisition and system investigation. The framework software architecture has been designed using component-based development principles to facilitate the framework extension and customization. Furthermore, for inter-component communication, the operating-system and programming language independent Common Object Request Broker Architecture (CORBA) [1] has been used which simplifies the integration of the framework into existing software environments.

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 bio-inspired - 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.