Proactive Human-Robot Interaction using Visuo-Lingual Transformers

• URL: http://arxiv.org/abs/2310.02506v1
• Date: Wed, 4 Oct 2023 00:50:21 GMT
• Title: Proactive Human-Robot Interaction using Visuo-Lingual Transformers
• Authors: Pranay Mathur
• Abstract summary: Humans possess the innate ability to extract latent visuo-lingual cues to infer context through human interaction. We propose a learning-based method that uses visual cues from the scene, lingual commands from a user and knowledge of prior object-object interaction to identify and proactively predict the underlying goal the user intends to achieve.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Humans possess the innate ability to extract latent visuo-lingual cues to infer context through human interaction. During collaboration, this enables proactive prediction of the underlying intention of a series of tasks. In contrast, robotic agents collaborating with humans naively follow elementary instructions to complete tasks or use specific hand-crafted triggers to initiate proactive collaboration when working towards the completion of a goal. Endowing such robots with the ability to reason about the end goal and proactively suggest intermediate tasks will engender a much more intuitive method for human-robot collaboration. To this end, we propose a learning-based method that uses visual cues from the scene, lingual commands from a user and knowledge of prior object-object interaction to identify and proactively predict the underlying goal the user intends to achieve. Specifically, we propose ViLing-MMT, a vision-language multimodal transformer-based architecture that captures inter and intra-modal dependencies to provide accurate scene descriptions and proactively suggest tasks where applicable. We evaluate our proposed model in simulation and real-world scenarios.

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