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

• URL: http://arxiv.org/abs/2503.12230v1
• Date: Sat, 15 Mar 2025 18:54:06 GMT
• Title: LIAM: Multimodal Transformer for Language Instructions, Images, Actions and Semantic Maps
• Authors: Yihao Wang, Raphael Memmesheimer, Sven Behnke,
• Abstract summary: We propose LIAM - an end-to-end model that predicts action transcripts based on language, image, action, and map inputs.<n>We evaluate our method on the ALFRED dataset, a simulator-generated benchmark for domestic tasks.
• Score: 18.602777449136738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• LiLMaps: Learnable Implicit Language Maps [18.342569823885864]
  We present an approach that enhances incremental implicit mapping through the integration of vision-language features.<n>Specifically, we (i) propose a decoder optimization technique for implicit language maps which can be used when new objects appear on the scene, and (ii) address the problem of inconsistent vision-language predictions between different viewing positions.
  arXiv  Detail & Related papers  (2025-01-06T16:04:56Z)
• Flex: End-to-End Text-Instructed Visual Navigation with Foundation Models [59.892436892964376]
  We investigate the minimal data requirements and architectural adaptations necessary to achieve robust closed-loop performance with vision-based control policies. Our findings are synthesized in Flex (Fly-lexically), a framework that uses pre-trained Vision Language Models (VLMs) as frozen patch-wise feature extractors. We demonstrate the effectiveness of this approach on quadrotor fly-to-target tasks, where agents trained via behavior cloning successfully generalize to real-world scenes.
  arXiv  Detail & Related papers  (2024-10-16T19:59:31Z)
• Object-Centric Instruction Augmentation for Robotic Manipulation [29.491990994901666]
  We introduce the textitObject-Centric Instruction Augmentation (OCI) framework to augment highly semantic and information-dense language instruction with position cues. We utilize a Multi-modal Large Language Model (MLLM) to weave knowledge of object locations into natural language instruction. We demonstrate that robotic manipulator imitation policies trained with our enhanced instructions outperform those relying solely on traditional language instructions.
  arXiv  Detail & Related papers  (2024-01-05T13:54:45Z)
• Interactive Planning Using Large Language Models for Partially Observable Robotics Tasks [54.60571399091711]
  Large Language Models (LLMs) have achieved impressive results in creating robotic agents for performing open vocabulary tasks. We present an interactive planning technique for partially observable tasks using LLMs.
  arXiv  Detail & Related papers  (2023-12-11T22:54:44Z)
• Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model [63.66204449776262]
  Instruct2Act is a framework that maps multi-modal instructions to sequential actions for robotic manipulation tasks. Our approach is adjustable and flexible in accommodating various instruction modalities and input types. Our zero-shot method outperformed many state-of-the-art learning-based policies in several tasks.
  arXiv  Detail & Related papers  (2023-05-18T17:59:49Z)
• ProgPrompt: Generating Situated Robot Task Plans using Large Language Models [68.57918965060787]
  Large language models (LLMs) can be used to score potential next actions during task planning. We present a programmatic LLM prompt structure that enables plan generation functional across situated environments.
  arXiv  Detail & Related papers  (2022-09-22T20:29:49Z)
• Learning Flexible Translation between Robot Actions and Language Descriptions [16.538887534958555]
  We propose a paired gated autoencoders (PGAE) for flexible translation between robot actions and language descriptions. We train our model in an end-to-end fashion by pairing each action with appropriate descriptions that contain a signal informing about the translation direction. With the option to use a pretrained language model as the language encoder, our model has the potential to recognise unseen natural language input.
  arXiv  Detail & Related papers  (2022-07-15T12:37:05Z)
• Pre-Trained Language Models for Interactive Decision-Making [72.77825666035203]
  We describe a framework for imitation learning in which goals and observations are represented as a sequence of embeddings. We demonstrate that this framework enables effective generalization across different environments. For test tasks involving novel goals or novel scenes, initializing policies with language models improves task completion rates by 43.6%.
  arXiv  Detail & Related papers  (2022-02-03T18:55:52Z)
• Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation [80.29069988090912]
  We study the problem of learning a range of vision-based manipulation tasks from a large offline dataset of robot interaction. We propose to leverage offline robot datasets with crowd-sourced natural language labels. We find that our approach outperforms both goal-image specifications and language conditioned imitation techniques by more than 25%.
  arXiv  Detail & Related papers  (2021-09-02T17:42:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.