Topic | Advancements in Embodied

2023-12-02  本文已影响0人  与阳光共进早餐

1. 写在前面

简略地了解一下基于LLMs的embodied AI进展

2. paper:Embodied Task Planning with Large Language Models (arxiv23)

2.1 basic info

2.2 main contribution

  1. Multimodal Dataset Construction
  1. Grounded Plan Tuning
  1. Extending Open-Vocabulary Object Detection
    Enhanced detection for multi-view RGB images, crucial for understanding scene context.

2.3 main idea

The TaPA framework integrates LLMs with visual information from open-vocabulary object detectors. It processes human instructions and available object lists to generate feasible action plans for navigation and manipulation tasks.

2.4 results

3. paper: Large Language Models as Generalizable Policies for Embodied Tasks (arxiv23)

3.1 basic info

3.2 main contribution

  1. LLaRP Framework
  1. Generalization Capabilities
  1. Language Rearrangement Benchmark
    Introduction of a new benchmark comprising 150,000 training tasks and 1,000 test tasks for language-conditioned rearrangement.

3.3 main idea

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4. else papers

:(之后有机会再针对每篇文章写一些详细的

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