Calibration of a physics-based model of an anthropomimetic robot using Evolution Strategies

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.