Using Language Models To Generate Whole-Body Multi-Contact Motions

We present a novel approach for generating sequences of whole-body poses with multi-contacts for humanoid robots, which is inspired by techniques from natural language processing. To this end, we propose a probabilistic n-gram language model learned from observation of human locomotion tasks. Human motion data is automatically segmented according to detected contacts of the body with the environment to provide support, that is, support poses, which are further subdivided with regard to whole-body configuration. These poses are subsequently used to train a language model, whose words are the poses, and whose sentences represent sequences of poses. Then, we propose a planning algorithm that, given the constraints imposed by a task, finds the sequence of transitions with the highest probability according to our language model. We have applied our approach to 140 motion capture recordings of locomotion tasks that involve using one or both hands for support. The evaluation demonstrates that our approach is able to generate complex sets of pose transitions, and shows promising results regarding its application to more complex tasks.