Multi-modal data-driven motion planning and synthesis

Abstract

We present a new approach for whole-body motion synthesis that is able to generate high-quality motions for challenging mobile-manipulation scenarios. Our approach decouples the problem in specialized locomotion and manipulation skills, and proposes a multi-modal planning scheme that explores the search space of each skill together with the possible transition points between skills. In order to achieve high-quality results the locomotion skill is designed to be fully data-driven, while manipulation skills can be algorithmic or data-driven according to data availability and the complexity of the environment. Our method is able to automatically generate complex motions with precise manipulation targets among obstacles and in coordination with locomotion.