Learning Multimodal Latent Dynamics for Human-Robot Interaction

TL;DR

This paper introduces a hybrid learning approach combining Hidden Markov Models and Variational Autoencoders to model and generate human-like, well-coordinated robot interactions based on human-human interaction data, with real-world validation.

Contribution

The novel integration of HMMs with VAEs for modeling HRI dynamics and the adaptive motion generation method that improves interaction quality and generalization.

Findings

Users perceive the robot interactions as more human-like and accurate.

The method outperforms baseline approaches in user preference studies.

Successful application in complex bimanual handover scenarios.

Abstract

This article presents a method for learning well-coordinated Human-Robot Interaction (HRI) from Human-Human Interactions (HHI). We devise a hybrid approach using Hidden Markov Models (HMMs) as the latent space priors for a Variational Autoencoder to model a joint distribution over the interacting agents. We leverage the interaction dynamics learned from HHI to learn HRI and incorporate the conditional generation of robot motions from human observations into the training, thereby predicting more accurate robot trajectories. The generated robot motions are further adapted with Inverse Kinematics to ensure the desired physical proximity with a human, combining the ease of joint space learning and accurate task space reachability. For contact-rich interactions, we modulate the robot's stiffness using HMM segmentation for a compliant interaction. We verify the effectiveness of our approach deployed on a Humanoid robot via a user study. Our method generalizes well to various humans despite being trained on data from just two humans. We find that users perceive our method as more human-like, timely, and accurate and rank our method with a higher degree of preference over other baselines. We additionally show the ability of our approach to generate successful interactions in a more complex scenario of Bimanual Robot-to-Human Handovers.