Summary of Vision-and-language Navigation Generative Pretrained Transformer, by Wen Hanlin
Vision-and-Language Navigation Generative Pretrained Transformer
by Wen Hanlin
First submitted to arxiv on: 27 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a new approach to Vision-and-Language Navigation (VLN) agents, which navigate real-world scenes guided by linguistic instructions. The challenge lies in enabling agents to adhere to instructions throughout the process of navigation. To address this, the authors introduce the Vision-and-Language Navigation Generative Pretrained Transformer (VLN-GPT), a transformer decoder model that models trajectory sequence dependencies without relying on historical encoding modules. This approach enhances efficiency and separates training into offline pre-training with imitation learning and online fine-tuning with reinforcement learning. The paper demonstrates the effectiveness of VLN-GPT, surpassing complex state-of-the-art encoder-based models on the VLN dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about helping machines understand instructions to navigate real-world scenes. It’s like a GPS system that knows where it has been and what actions it took to get there. The problem is that current approaches make these machines too complicated and use too many resources. The authors suggest using a special type of model called VLN-GPT, which can remember its path without needing extra storage. This makes the machine more efficient. They also train the machine in two stages: first, they teach it to imitate what a good navigation system would do, and then they fine-tune it to make decisions on its own. The results show that VLN-GPT is better than other approaches at navigating real-world scenes. |
Keywords
» Artificial intelligence » Decoder » Encoder » Fine tuning » Gpt » Reinforcement learning » Transformer
