Summary of Same: Learning Generic Language-guided Visual Navigation with State-adaptive Mixture Of Experts, by Gengze Zhou et al.
SAME: Learning Generic Language-Guided Visual Navigation with State-Adaptive Mixture of Experts
by Gengze Zhou, Yicong Hong, Zun Wang, Chongyang Zhao, Mohit Bansal, Qi Wu
First submitted to arxiv on: 7 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); 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 proposed State-Adaptive Mixture of Experts (SAME) model is a novel approach to learning instruction-guided visual navigation, consolidating diverse tasks into a unified framework. The model effectively enables an agent to infer decisions based on different-granularity language and dynamic observations, outperforming or achieving comparable performance to task-specific agents in seven simultaneous navigation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a single framework for learning navigation tasks that differ in their focus on exploration or following textual commands. A new State-Adaptive Mixture of Experts (SAME) model is introduced to help an agent make decisions based on language and observations, which performs well across multiple tasks. |
Keywords
» Artificial intelligence » Mixture of experts
