Summary of Multimodal Information Bottleneck For Deep Reinforcement Learning with Multiple Sensors, by Bang You et al.
Multimodal Information Bottleneck for Deep Reinforcement Learning with Multiple Sensors
by Bang You, Huaping Liu
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 multimodal information bottleneck model leverages information from multiple sensory modalities, including egocentric images and proprioception, to learn task-relevant joint representations. By compressing and retaining predictive information in multimodal observations, the model fuses complementary information and filters out task-irrelevant information, leading to better sample efficiency and zero-shot robustness on locomotion tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses machine learning to help robots make decisions based on what they see and feel. It’s like trying to understand what a person is thinking by looking at their face and knowing how they’re moving their body. The idea is to use multiple sources of information, like pictures and sensor data, to learn more about the world and make better choices. This can help robots do things like walk or run without getting stuck. |
Keywords
* Artificial intelligence * Machine learning * Zero shot
