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arXiv · 2304.00584

An End-to-End Human Simulator for Task-Oriented Multimodal Human-Robot Collaboration

Afagh Mehri Shervedani·Siyu Li·Natawut Monaikul·Bahareh Abbasi·Barbara Di Eugenio·Milos Zefran
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Abstract

This paper proposes a neural network-based user simulator that can provide a multimodal interactive environment for training Reinforcement Learning (RL) agents in collaborative tasks involving multiple modes of communication. The simulator is trained on the existing ELDERLY-AT-HOME corpus and accommodates multiple modalities such as language, pointing gestures, and haptic-ostensive actions. The paper also presents a novel multimodal data augmentation approach, which addresses the challenge of using a limited dataset due to the expensive and time-consuming nature of collecting human demonstrations. Overall, the study highlights the potential for using RL and multimodal user simulators in developing and improving domestic assistive robots.

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Afagh Mehri Shervedani, Siyu Li, Natawut Monaikul, Bahareh Abbasi, Barbara Di Eugenio, Milos Zefran. 2023-04-02. An End-to-End Human Simulator for Task-Oriented Multimodal Human-Robot Collaboration. https://arxiv.org/abs/2304.00584

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