Optimization and Imitation Problems for Humanoid Robots

In this paper, the problems of humanoid robot motion optimization and human motion imitation by a humanoid robot are investigated. At first, we propose a unified framework for the optimization of humanoid robot motions. This framework is based on an efficient dynamics algorithmwhich allows the calculation of the gradient function with respect to the control parameters analytically. We show the efficiency of the framework through an example of smoothing a pre-calculated humanoid motion by minimizing the exerted torques, and at the same time improving the stability of the humanoid robot during the execution of the motion. Furthermore, we give insights into the problem of imitating human capture motions by a humanoid robot. We point out that the imitation problem can be formulated as an optimization problem under the constraints of physical limits and balance. The experimental results conducted on the humanoid robot HRP-2 have pointed out the efficiency of the framework of optimization to smooth humanoid robot motions and to generate imitated motions that preserve the salient characteristics of the original human captured motions. Moreover the experiments showed that the optimization procedure is well converging thanks to the analytical computation of the gradient function.