MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments

• URL: http://arxiv.org/abs/2402.00290v3
• Date: Mon, 29 Jul 2024 05:47:44 GMT
• Title: MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments
• Authors: Yang Liu, Xinshuai Song, Kaixuan Jiang, Weixing Chen, Jingzhou Luo, Guanbin Li, Liang Lin,
• Abstract summary: We introduce the Multimodal Embodied Interactive Agent (MEIA), capable of translating high-level tasks expressed in natural language into a sequence of executable actions. MEM module enables MEIA to generate executable action plans based on diverse requirements and the robot's capabilities.
• Score: 82.67236400004826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the surge in the development of large language models, embodied intelligence has attracted increasing attention. Nevertheless, prior works on embodied intelligence typically encode scene or historical memory in an unimodal manner, either visual or linguistic, which complicates the alignment of the model's action planning with embodied control. To overcome this limitation, we introduce the Multimodal Embodied Interactive Agent (MEIA), capable of translating high-level tasks expressed in natural language into a sequence of executable actions. Specifically, we propose a novel Multimodal Environment Memory (MEM) module, facilitating the integration of embodied control with large models through the visual-language memory of scenes. This capability enables MEIA to generate executable action plans based on diverse requirements and the robot's capabilities. Furthermore, we construct an embodied question answering dataset based on a dynamic virtual cafe environment with the help of the large language model. In this virtual environment, we conduct several experiments, utilizing multiple large models through zero-shot learning, and carefully design scenarios for various situations. The experimental results showcase the promising performance of our MEIA in various embodied interactive tasks.

Related papers

• LangSuitE: Planning, Controlling and Interacting with Large Language Models in Embodied Text Environments [70.91258869156353]
  We introduce LangSuitE, a versatile and simulation-free testbed featuring 6 representative embodied tasks in textual embodied worlds. Compared with previous LLM-based testbeds, LangSuitE offers adaptability to diverse environments without multiple simulation engines. We devise a novel chain-of-thought (CoT) schema, EmMem, which summarizes embodied states w.r.t. history information.
  arXiv  Detail & Related papers  (2024-06-24T03:36:29Z)
• Bridging Language, Vision and Action: Multimodal VAEs in Robotic Manipulation Tasks [0.0]
  In this work, we focus on unsupervised vision-language--action mapping in the area of robotic manipulation. We propose a model-invariant training alternative that improves the models' performance in a simulator by up to 55%. Our work thus also sheds light on the potential benefits and limitations of using the current multimodal VAEs for unsupervised learning of robotic motion trajectories.
  arXiv  Detail & Related papers  (2024-04-02T13:25:16Z)
• On the Multi-turn Instruction Following for Conversational Web Agents [83.51251174629084]
  We introduce a new task of Conversational Web Navigation, which necessitates sophisticated interactions that span multiple turns with both the users and the environment. We propose a novel framework, named self-reflective memory-augmented planning (Self-MAP), which employs memory utilization and self-reflection techniques.
  arXiv  Detail & Related papers  (2024-02-23T02:18:12Z)
• An Interactive Agent Foundation Model [49.77861810045509]
  We propose an Interactive Agent Foundation Model that uses a novel multi-task agent training paradigm for training AI agents. Our training paradigm unifies diverse pre-training strategies, including visual masked auto-encoders, language modeling, and next-action prediction. We demonstrate the performance of our framework across three separate domains -- Robotics, Gaming AI, and Healthcare.
  arXiv  Detail & Related papers  (2024-02-08T18:58:02Z)
• Veagle: Advancements in Multimodal Representation Learning [0.0]
  This paper introduces a novel approach to enhance the multimodal capabilities of existing models. Our proposed model Veagle, incorporates a unique mechanism inspired by the successes and insights of previous works. Our results indicate a improvement of 5-6 % in performance, with Veagle outperforming existing models by a notable margin.
  arXiv  Detail & Related papers  (2024-01-18T12:45:25Z)
• Expanding Frozen Vision-Language Models without Retraining: Towards Improved Robot Perception [0.0]
  Vision-language models (VLMs) have shown powerful capabilities in visual question answering and reasoning tasks. In this paper, we demonstrate a method of aligning the embedding spaces of different modalities to the vision embedding space. We show that using multiple modalities as input improves the VLM's scene understanding and enhances its overall performance in various tasks.
  arXiv  Detail & Related papers  (2023-08-31T06:53:55Z)
• MM-REACT: Prompting ChatGPT for Multimodal Reasoning and Action [96.33509740612486]
  MM-REACT is a system paradigm that integrates ChatGPT with a pool of vision experts to achieve multimodal reasoning and action. MM-REACT's prompt design allows language models to accept, associate, and process multimodal information.
  arXiv  Detail & Related papers  (2023-03-20T18:31:47Z)
• Chat with the Environment: Interactive Multimodal Perception Using Large Language Models [19.623070762485494]
  Large Language Models (LLMs) have shown remarkable reasoning ability in few-shot robotic planning. Our study demonstrates that LLMs can provide high-level planning and reasoning skills and control interactive robot behavior in a multimodal environment.
  arXiv  Detail & Related papers  (2023-03-14T23:01:27Z)
• PaLM-E: An Embodied Multimodal Language Model [101.29116156731762]
  We propose embodied language models to incorporate real-world continuous sensor modalities into language models. We train these encodings end-to-end, in conjunction with a pre-trained large language model, for multiple embodied tasks. Our largest model, PaLM-E-562B with 562B parameters, is a visual-language generalist with state-of-the-art performance on OK-VQA.
  arXiv  Detail & Related papers  (2023-03-06T18:58:06Z)

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.