Summary of Two-stage Depth Enhanced Learning with Obstacle Map For Object Navigation, by Yanwei Zheng et al.
Two-Stage Depth Enhanced Learning with Obstacle Map For Object Navigation
by Yanwei Zheng, Shaopu Feng, Bowen Huang, Changrui Li, Xiao Zhang, Dongxiao Yu
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 proposed visual object navigation (VON) framework enables agents to navigate to specific objects while leveraging RGB, depth, and obstacle information. By separating searching and navigating stages, the model improves navigation performance and training efficiency. The approach utilizes a two-stage reward system, where the agent explores larger areas in the searching stage and seeks optimal paths in the navigating stage. To further enhance navigation efficiency, the feature extractor is pre-trained using RGB and depth information from the training scene, while obstacle information is memorized during navigation to reduce collisions and deadlocks. The policy network takes concatenated prior knowledge as input and outputs navigation actions under two-stage rewards. Experimental results on AI2-Thor and RoboTHOR demonstrate significant improvements over state-of-the-art methods in terms of success rate and navigation efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores a new way for machines to find objects by looking at what they see. This is called visual object navigation (VON). The problem with this task is that the machine doesn’t know where it’s going, so it needs to figure out how to get there. The researchers came up with a solution that lets the machine explore more during the search stage and find the best path during the navigate stage. They also taught the machine to use information from the environment, like what’s in front of it, to avoid getting stuck or crashing into things. By using this new approach, the machine was able to find objects more efficiently than other machines that were doing it a different way. |
