Christoph Maurer

Vittorio Lippi, Christoph Maurer, Thomas Mergner

Posture control and balance are basic requirements for a humanoid robot performing motor tasks like walking and interacting with the environment. For this reason, posture control is one of the elements taken into account when evaluating the performance of humanoids. In this work, we describe and analyze a performance indicator based on the comparison between the body sway of a robot standing on a moving surface and the one of healthy subjects performing the same experiment. This approach is here oriented to the evaluation of human likeness. The measure is tested with three human-inspired humanoid posture control systems, the independent channel (IC), the disturbance identification and compensation (DEC), and the eigenmovement (EM) control. The potential and the limitations connected with such human-inspired humanoid control mechanisms are then discussed.

Vittorio Lippi, Thomas Mergner, Thomas Seel et al.

This work presents a system to benchmark humanoid posture control and balance performances under perturbed conditions. The specific benchmarking scenario consists, for example, of balancing upright stance while performing voluntary movements on moving surfaces. The system includes a motion platform used to provide the perturbation, an innovative body-tracking system suitable for robots, humans and exoskeletons, control software and a set of predefined perturbations, a humanoid robot used to test algorithms, and analysis software providing state of the art data analysis used to provide quantitative measures of performance. In order to provide versatility, the design of the system is oriented to modularity: all its components can be replaced or extended according to experimental needs, adding additional perturbation profiles, new evaluation principles, and alternative tracking systems. It will be possible to use the system with different kinds of robots and exoskeletons as well as for human experiments aimed at gaining insights into human balance capabilities.

Vittorio Lippi, Christoph Maurer, Thomas Mergner

This work presents a system identification procedure based on Convolutional Neural Networks (CNN) for human posture control using the DEC (Disturbance Estimation and Compensation) parametric model. The modular structure of the proposed control model inspired the design of a modular identification procedure, in the sense that the same neural network is used to identify the parameters of the modules controlling different degrees of freedom. In this way the presented examples of body sway induced by external stimuli provide several training samples at once.

Vittorio Lippi, Thomas Mergner, Christoph Maurer

In this work we present a system identification procedure based on Convolutional Neural Networks (CNN) for human posture control models. A usual approach to the study of human posture control consists in the identification of parameters for a control system. In this context, linear models are particularly popular due to the relative simplicity in identifying the required parameters and to analyze the results. Nonlinear models, conversely, are required to predict the real behavior exhibited by human subjects and hence it is desirable to use them in posture control analysis. The use of CNN aims to overcome the heavy computational requirement for the identification of nonlinear models, in order to make the analysis of experimental data less time consuming and, in perspective, to make such analysis feasible in the context of clinical tests. Some potential implications of the method for humanoid robotics are also discussed.