Fast Direct Optimal Control for Humanoids Based on Dynamics Representation in FPC Latent Space

This study introduces a novel approach to Humanoid Robot Motion Generation using Functional Principal Component Analysis (FPCA) within the framework of Direct Optimal Control (DOC). FPCA efficiently compresses high-dimensional motion data, including ground reaction forces, into a low-dimensional space known as the FPC space. These low-dimensional elements preserve the essential characteristics of the original motions and allow for the synthesis of specific elements to create entirely new representations. By using these low-dimensional elements as optimization variables, we anticipated a significant reduction in computational time for motion generation. Experiments on the humanoid robot HRP-4 J, considering various objective functions, demonstrated the method's applicability for generating whole-body motions. The Root Mean Square Error (RMSE) for angle data between the proposed method and conventional methods yielded an average RMSE of 0.007 rd, indicating precise motion generation without compromising data characteristics. Moreover, the proposed method's computation time was 35.8 times faster than the conventional approach.