Generation of Human-like Arm Motions using Sampling-based Motion Planning

Natural and human-like arm motions are promising features to facilitate social understanding of humanoid robots. To this end, we integrate biophysical characteristics of human arm-motions into sampling-based motion planning. We show the generality of our method by evaluating it with multiple manipulators. Our first contribution is to introduce a set of cost functions to optimize for human-like arm postures during collision-free motion planning. In a subsequent step, an optimization phase is used to improve the human-likeness of the initial path. Additionally, we present an interpolation approach for generating obstacle-aware and multi-modal velocity profiles. We thus generate collision-free and human-like motions in narrow passages while allowing for natural acceleration in free space.