Bridging Language, Vision and Action: Multimodal VAEs in Robotic Manipulation Tasks

arXiv:2404.01932v1 Announce Type: cross
Abstract: In this work, we focus on unsupervised vision-language-action mapping in the area of robotic manipulation. Recently, multiple approaches employing pre-trained large language and vision models have been proposed for this task. However, they are computationally demanding and require careful fine-tuning of the produced outputs. A more lightweight alternative would be the implementation of multimodal Variational Autoencoders (VAEs) which can extract the latent features of the data and integrate them into a joint representation, as has …

abstract arxiv cs.lg cs.ro fine-tuning focus however language large language manipulation mapping multimodal multiple robotic robotic manipulation tasks type unsupervised vision vision models work