# Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks

arXiv: https://arxiv.org/abs/1908.04955 · HTML: https://mlsanity.com/paper/1908.04955/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 6 of 6)

- [Language-Conditioned Imitation Learning for Robot Manipulation Tasks](https://mlsanity.com/paper/2010.12083/) (2020)
- [A System for Imitation Learning of Contact-Rich Bimanual Manipulation Policies](https://mlsanity.com/paper/2208.00596/) (2022)
- [A Survey on Imitation Learning for Contact-Rich Tasks in Robotics](https://mlsanity.com/paper/2506.13498/) (2025)
- [GRID: A Platform for General Robot Intelligence Development](https://mlsanity.com/paper/2310.00887/) (2023)
- [Understanding Your Agent: Leveraging Large Language Models for Behavior Explanation](https://mlsanity.com/paper/2311.18062/) (2023)
- [Learning and Blending Robot Hugging Behaviors in Time and Space](https://mlsanity.com/paper/2212.01507/) (2022)

## Abstract (arXiv)

Human-robot interaction benefits greatly from multimodal sensor inputs as they enable increased robustness and generalization accuracy. Despite this observation, few HRI methods are capable of efficiently performing inference for multimodal systems. In this work, we introduce a reformulation of Interaction Primitives which allows for learning from demonstration of interaction tasks, while also gracefully handling nonlinearities inherent to multimodal inference in such scenarios. We also empirically show that our method results in more accurate, more robust, and faster inference than standard Interaction Primitives and other common methods in challenging HRI scenarios.