Latent Representation in Human-Robot Interaction with Explicit Consideration of Periodic Dynamics

TL;DR

This paper introduces a novel data-driven framework combining VRNN and reservoir computing to analyze and predict periodic human-robot interactions, effectively capturing latent dynamics and distinguishing different periodic motions.

Contribution

The paper develops an augmented VRNN framework with reservoir computing to explicitly model periodic dynamics in human-robot interaction data.

Findings

Achieved accurate prediction of human observations and robot actions.

Successfully distinguished different periodic motions in latent space.

Validated framework with rope-rotation/swinging experiment data.

Abstract

This paper presents a new data-driven framework for analyzing periodic physical human-robot interaction (pHRI) in latent state space. To elaborate human understanding and/or robot control during pHRI, the model representing pHRI is critical. Recent developments of deep learning technologies would enable us to learn such a model from a dataset collected from the actual pHRI. Our framework is developed based on variational recurrent neural network (VRNN), which can inherently handle time-series data like one pHRI generates. This paper modifies VRNN in order to include the latent dynamics from robot to human explicitly. In addition, to analyze periodic motions like walking, we integrate a new recurrent network based on reservoir computing (RC), which has random and fixed connections between numerous neurons, with VRNN. By augmenting RC into complex domain, periodic behavior can be represented as the phase rotation in complex domain without decaying the amplitude. For verification of the proposed framework, a rope-rotation/swinging experiment was analyzed. The proposed framework, trained on the dataset collected from the experiment, achieved the latent state space where the differences in periodic motions can be distinguished. Such a well-distinguished space yielded the best prediction accuracy of the human observations and the robot actions. The attached video can be seen in youtube: https://youtu.be/umn0MVcIpsY