FLAME: Learning to Navigate with Multimodal LLM in Urban Environments

• URL: http://arxiv.org/abs/2408.11051v2
• Date: Tue, 21 Jan 2025 04:06:09 GMT
• Title: FLAME: Learning to Navigate with Multimodal LLM in Urban Environments
• Authors: Yunzhe Xu, Yiyuan Pan, Zhe Liu, Hesheng Wang,
• Abstract summary: Large Language Models (LLMs) have demonstrated potential in Vision-and-Language Navigation (VLN) tasks. LLMs struggle with specialized navigation tasks, yielding suboptimal performance compared to specialized VLN models. We introduce FLAME, a novel Multimodal LLM-based agent and architecture designed for urban VLN tasks.
• Score: 12.428873051106702
• License:
• Abstract: Large Language Models (LLMs) have demonstrated potential in Vision-and-Language Navigation (VLN) tasks, yet current applications face challenges. While LLMs excel in general conversation scenarios, they struggle with specialized navigation tasks, yielding suboptimal performance compared to specialized VLN models. We introduce FLAME (FLAMingo-Architected Embodied Agent), a novel Multimodal LLM-based agent and architecture designed for urban VLN tasks that efficiently handles multiple observations. Our approach implements a three-phase tuning technique for effective adaptation to navigation tasks, including single perception tuning for street view description, multiple perception tuning for route summarization, and end-to-end training on VLN datasets. The augmented datasets are synthesized automatically. Experimental results demonstrate FLAME's superiority over existing methods, surpassing state-of-the-art methods by a 7.3% increase in task completion on Touchdown dataset. This work showcases the potential of Multimodal LLMs (MLLMs) in complex navigation tasks, representing an advancement towards applications of MLLMs in the field of embodied intelligence.

Related papers

• Task Preference Optimization: Improving Multimodal Large Language Models with Vision Task Alignment [58.94611347128066]
  Task Preference Optimization (TPO) is a novel method that utilizes differentiable task preferences derived from typical fine-grained visual tasks. By leveraging rich visual labels during training, TPO significantly enhances the MLLM's multimodal capabilities and task-specific performance. Our instantiation of this approach with VideoChat and LLaVA demonstrates an overall 14.6% improvement in multimodal performance compared to baseline models.
  arXiv  Detail & Related papers  (2024-12-26T18:56:05Z)
• Multimodal LLM for Intelligent Transportation Systems [0.0]
  This paper introduces a novel 3-dimensional framework that encapsulates the intersection of applications, machine learning methodologies, and hardware devices. Instead of using multiple machine learning algorithms, our framework uses a single, data-centric LLM architecture that can analyze time series, images, and videos. We apply this LLM framework to different sensor datasets, including time-series data and visual data from sources like Oxford Radar RobotCar, D-Behavior (D-Set), nuScenes by Motional, and Comma2k19.
  arXiv  Detail & Related papers  (2024-12-16T11:50:30Z)
• MLLM as Retriever: Interactively Learning Multimodal Retrieval for Embodied Agents [28.419007116364668]
  MLLM agents demonstrate potential for complex embodied tasks by retrieving multimodal task-relevant trajectory data. Current retrieval methods primarily focus on surface-level similarities of textual or visual cues in trajectories, neglecting their effectiveness for the specific task at hand. We propose a novel method, MLLM as ReTriever (MART), which enhances the performance of embodied agents by utilizing interaction data to fine-tune an MLLM retriever.
  arXiv  Detail & Related papers  (2024-10-04T14:10:39Z)
• Rethinking VLMs and LLMs for Image Classification [6.550471260627169]
  Large Language Models (LLMs) are increasingly being merged with Visual Language Models (VLMs) to enable new capabilities. We show that, for object and scene recognition, VLMs that do not leverage LLMs can achieve better performance than VLMs that do. We propose a pragmatic solution: a lightweight fix involving a relatively small LLM that efficiently routes visual tasks to the most suitable model for the task.
  arXiv  Detail & Related papers  (2024-10-03T23:40:21Z)
• NVLM: Open Frontier-Class Multimodal LLMs [64.00053046838225]
  We introduce NVLM 1.0, a family of frontier-class multimodal large language models (LLMs) that achieve state-of-the-art results on vision-language tasks. We propose a novel architecture that enhances both training efficiency and multimodal reasoning capabilities. We develop production-grade multimodality for the NVLM-1.0 models, enabling them to excel in vision-language tasks.
  arXiv  Detail & Related papers  (2024-09-17T17:59:06Z)
• SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning [70.21358720599821]
  Large language models (LLMs) hold the promise of solving diverse tasks when provided with appropriate natural language prompts. We propose SELF-GUIDE, a multi-stage mechanism in which we synthesize task-specific input-output pairs from the student LLM. We report an absolute improvement of approximately 15% for classification tasks and 18% for generation tasks in the benchmark's metrics.
  arXiv  Detail & Related papers  (2024-07-16T04:41:58Z)
• Efficient Prompting for LLM-based Generative Internet of Things [88.84327500311464]
  Large language models (LLMs) have demonstrated remarkable capacities on various tasks, and integrating the capacities of LLMs into the Internet of Things (IoT) applications has drawn much research attention recently. Due to security concerns, many institutions avoid accessing state-of-the-art commercial LLM services, requiring the deployment and utilization of open-source LLMs in a local network setting. We propose a LLM-based Generative IoT (GIoT) system deployed in the local network setting in this study.
  arXiv  Detail & Related papers  (2024-06-14T19:24:00Z)
• Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models [87.47400128150032]
  We propose a novel LMM architecture named Lumen, a Large multimodal model with versatile vision-centric capability enhancement. Lumen first promotes fine-grained vision-language concept alignment. Then the task-specific decoding is carried out by flexibly routing the shared representation to lightweight task decoders.
  arXiv  Detail & Related papers  (2024-03-12T04:13:45Z)
• InfMLLM: A Unified Framework for Visual-Language Tasks [44.29407348046122]
  multimodal large language models (MLLMs) have attracted growing interest. This work delves into enabling LLMs to tackle more vision-language-related tasks. InfMLLM achieves either state-of-the-art (SOTA) performance or performance comparable to recent MLLMs.
  arXiv  Detail & Related papers  (2023-11-12T09:58:16Z)
• VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View [81.58612867186633]
  Vision and Language Navigation(VLN) requires visual and natural language understanding as well as spatial and temporal reasoning capabilities. We show that VELMA is able to successfully follow navigation instructions in Street View with only two in-context examples. We further finetune the LLM agent on a few thousand examples and achieve 25%-30% relative improvement in task completion over the previous state-of-the-art for two datasets.
  arXiv  Detail & Related papers  (2023-07-12T11:08:24Z)

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.