L4RW: Laziness-based Realistic Real-time Responsive Rebalance in Walking

Abstract

We present a novel L4RW (Laziness-based Realistic Real-time Responsive Rebalance in Walking) technique to synthesize 4RW animations under unexpected external perturbations with minimal locomotion effort. We first devise a lazy dynamic rebalance model, which specifies the dynamic balance conditions, defines the rebalance effort, and selects the suitable rebalance strategy automatically using the laziness law after an unexpected perturbation. Based on the model, L4RW searches over a motion capture (mocap) database for an appropriate motion segment to follow, and the transition-to motions is generated by interpolating the active response dynamic motion. A support vector machine (SVM) based training, classification, and predication algorithm is applied to reduce the search space, and it is trained offline only once. Our algorithm classifies the mocap database into many rebalance strategy-specified subsets and then online predicts responsive motions in the subset according to the selected strategy. The rebalance effort, the extrapolated center of mass (XCoM) and environment constraints are selected as feature attributes for the SVM feature vector. Furthermore, the subset s segments are sorted through the rebalance effort, then our algorithm searches for an acceptable segment starting from the least-effort segment. Compared with previous methods, our search increases speed by over two orders of magnitude, and our algorithm creates more realistic and smooth 4RW animation.