P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task

• URL: http://arxiv.org/abs/2409.11279v1
• Date: Tue, 17 Sep 2024 15:29:34 GMT
• Title: P-RAG: Progressive Retrieval Augmented Generation For Planning on Embodied Everyday Task
• Authors: Weiye Xu, Min Wang, Wengang Zhou, Houqiang Li,
• Abstract summary: Embodied Everyday Task is a popular task in the embodied AI community. Natural language instructions often lack explicit task planning. Extensive training is required to equip models with knowledge of the task environment.
• Score: 94.08478298711789
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Embodied Everyday Task is a popular task in the embodied AI community, requiring agents to make a sequence of actions based on natural language instructions and visual observations. Traditional learning-based approaches face two challenges. Firstly, natural language instructions often lack explicit task planning. Secondly, extensive training is required to equip models with knowledge of the task environment. Previous works based on Large Language Model (LLM) either suffer from poor performance due to the lack of task-specific knowledge or rely on ground truth as few-shot samples. To address the above limitations, we propose a novel approach called Progressive Retrieval Augmented Generation (P-RAG), which not only effectively leverages the powerful language processing capabilities of LLMs but also progressively accumulates task-specific knowledge without ground-truth. Compared to the conventional RAG methods, which retrieve relevant information from the database in a one-shot manner to assist generation, P-RAG introduces an iterative approach to progressively update the database. In each iteration, P-RAG retrieves the latest database and obtains historical information from the previous interaction as experiential references for the current interaction. Moreover, we also introduce a more granular retrieval scheme that not only retrieves similar tasks but also incorporates retrieval of similar situations to provide more valuable reference experiences. Extensive experiments reveal that P-RAG achieves competitive results without utilizing ground truth and can even further improve performance through self-iterations.

Related papers

• AssistRAG: Boosting the Potential of Large Language Models with an Intelligent Information Assistant [23.366991558162695]
  Large Language Models generate factually incorrect information, known as "hallucination" To cope with these challenges, we propose Assistant-based Retrieval-Augmented Generation (AssistRAG) This assistant manages memory and knowledge through tool usage, action execution, memory building, and plan specification.
  arXiv  Detail & Related papers  (2024-11-11T09:03:52Z)
• Spatial Reasoning and Planning for Deep Embodied Agents [2.7195102129095003]
  This thesis explores the development of data-driven techniques for spatial reasoning and planning tasks. It focuses on enhancing learning efficiency, interpretability, and transferability across novel scenarios.
  arXiv  Detail & Related papers  (2024-09-28T23:05:56Z)
• MemoRAG: Moving towards Next-Gen RAG Via Memory-Inspired Knowledge Discovery [24.38640001674072]
  Retrieval-Augmented Generation (RAG) leverages retrieval tools to access external databases. Existing RAG systems are primarily effective for straightforward question-answering tasks. We propose MemoRAG, a novel retrieval-augmented generation paradigm empowered by long-term memory.
  arXiv  Detail & Related papers  (2024-09-09T13:20:31Z)
• Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment [16.39696580487218]
  Pretrained language models like BERT and T5 serve as crucial backbone encoders for dense retrieval. Recent research has explored using large language models (LLMs) as retrievers, achieving SOTA performance across various tasks.
  arXiv  Detail & Related papers  (2024-08-22T08:16:07Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Zero-shot Item-based Recommendation via Multi-task Product Knowledge Graph Pre-Training [106.85813323510783]
  This paper presents a novel paradigm for the Zero-Shot Item-based Recommendation (ZSIR) task. It pre-trains a model on product knowledge graph (PKG) to refine the item features from PLMs. We identify three challenges for pre-training PKG, which are multi-type relations in PKG, semantic divergence between item generic information and relations and domain discrepancy from PKG to downstream ZSIR task.
  arXiv  Detail & Related papers  (2023-05-12T17:38:24Z)
• Task Residual for Tuning Vision-Language Models [69.22958802711017]
  We propose a new efficient tuning approach for vision-language models (VLMs) named Task Residual Tuning (TaskRes) TaskRes explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task. The proposed TaskRes is simple yet effective, which significantly outperforms previous methods on 11 benchmark datasets.
  arXiv  Detail & Related papers  (2022-11-18T15:09:03Z)
• Parrot: Data-Driven Behavioral Priors for Reinforcement Learning [79.32403825036792]
  We propose a method for pre-training behavioral priors that can capture complex input-output relationships observed in successful trials. We show how this learned prior can be used for rapidly learning new tasks without impeding the RL agent's ability to try out novel behaviors.
  arXiv  Detail & Related papers  (2020-11-19T18:47:40Z)
• Continual Learning for Natural Language Generation in Task-oriented Dialog Systems [72.92029584113676]
  Natural language generation (NLG) is an essential component of task-oriented dialog systems. We study NLG in a "continual learning" setting to expand its knowledge to new domains or functionalities incrementally. The major challenge towards this goal is catastrophic forgetting, meaning that a continually trained model tends to forget the knowledge it has learned before.
  arXiv  Detail & Related papers  (2020-10-02T10:32:29Z)

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.