Where Am I and Where Should I Go? Grounding Positional and Directional Labels in a Disoriented Human Balancing Task

In this paper, we present an approach toward grounding linguistic positional and directional labels directly to human motions in the course of a disoriented balancing task in a multi-axis rotational device. We use deep neural models to predict human subjects’ joystick motions as well as the subjects’ proficiency in the task, combined with BERT embedding vectors for positional and directional labels extracted from annotations into an embodied direction classifier. We find that combining contextualized BERT embeddings with embeddings describing human motion and proficiency can successfully predict the direction a hypothetical human participant should move to achieve better balance with accuracy that is comparable to a moderately-proficient balancing task subject, and that our combined embodied model may actually make decisions that are objectively better than decisions made by some humans.