Inverse Dynamics Filtering for Sampling-based Motion Control

We improve the sampling-based motion control method proposed by Liu et al. using inverse dynamics. To deal with noise in the motion capture we filter the motion data using a Butterworth filter where we choose the cutoff frequency such that the zero-moment point falls within the support polygon for the greatest number of frames. We discuss how to detect foot contact for foot and ground optimization and inverse dynamics, and we optimize to increase the area of supporting polygon. Sample simulations receive filtered inverse dynamics torques at frames where the ZMP is sufficiently close to the support polygon, which simplifies the problem of finding the PD targets that produce physically valid control matching the target motion. We test our method on different motions and we demonstrate that our method has lower error, higher success rates, and generally produces smoother results.