{LIAM}: Multimodal Transformer for Language Instructions, Images, Actions and Semantic Maps

The availability of large language models and open-vocabulary object perception methods enables more flexibility for domestic service robots. The large variability of domestic tasks can be addressed without implementing each task individually by providing the robot with a task description along with appropriate environment information. In this work, we propose {LIAM} – an end-to-end model that predicts action transcripts based on language, image, action, and map inputs. Language and image inputs are encoded with a {CLIP} backbone, for which we designed two pre-training tasks to fine-tune its weights and pre-align the latent spaces. We evaluate our method on the {ALFRED} dataset, a simulator-generated benchmark for domestic tasks. Our results demonstrate the importance of pre-aligning embedding spaces from different modalities and the efficacy of incorporating semantic maps.