Summary of Towards Deviation-robust Agent Navigation Via Perturbation-aware Contrastive Learning, by Bingqian Lin et al.
Towards Deviation-Robust Agent Navigation via Perturbation-Aware Contrastive Learning
by Bingqian Lin, Yanxin Long, Yi Zhu, Fengda Zhu, Xiaodan Liang, Qixiang Ye, Liang Lin
First submitted to arxiv on: 9 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 A novel approach to enhancing the generalization ability of vision-and-language navigation (VLN) agents is presented. Existing VLN agents are typically trained under disturbance-free environments, which can lead to poor performance in real-world scenarios where unexpected route deviations occur due to obstacles or human interruptions. The proposed Progressive Perturbation-aware Contrastive Learning (PROPER) model-agnostic training paradigm requires VLN agents to learn deviation-robust navigation by navigating through perturbed trajectories. A path perturbation scheme is introduced to implement route deviations, and a progressively perturbed trajectory augmentation strategy allows the agent to self-adaptively learn to navigate under perturbation. A perturbation-aware contrastive learning mechanism is developed to encourage the agent to capture the difference brought by perturbation. The proposed approach is evaluated on R2R and Path-Perturbed R2R (PP-R2R) datasets, showing improved navigation robustness for multiple VLN baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re giving a friend directions to navigate through a 3D environment using language instructions. But what if there are obstacles or distractions along the way? This paper explores how AI agents can learn to adapt to these unexpected changes and still follow the original instructions. The authors propose a new training method called PROPER that helps AI agents develop this ability by simulating different scenarios where route deviations might occur. They test their approach on real-world data and show that it improves the performance of AI agents in navigating through complex environments. |
Keywords
» Artificial intelligence » Generalization
