Summary of Quick and Accurate Affordance Learning, by Fedor Scholz et al.
Quick and Accurate Affordance Learning
by Fedor Scholz, Erik Ayari, Johannes Bertram, Martin V. Butz
First submitted to arxiv on: 13 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 deep learning architecture models infant-like behavior by actively exploring environments and learning about their affordances. The model mediates between global cognitive map exploration and local affordance learning, using uncertainty measures to guide the simulated agent towards regions of expected knowledge gain. Three measures of uncertainty are contrasted: predicted uncertainty, standard deviation (SD), and Jensen-Shannon Divergence (JSD) between models. The study shows that SD and JSD focus on epistemic uncertainty, while predicted uncertainty gets fooled by aleatoric uncertainty. The findings suggest three key ingredients for coordinating active learning curricula: navigation behavior coordination with local motor behavior, affordance encoding locally, and density comparison techniques for estimating expected knowledge gain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Infants learn by actively exploring their environments and learning about what they can do. This paper uses a special kind of computer program to model this type of learning. The program helps the simulated agent find new things to learn by choosing where to go based on how much it thinks it will learn. Three different ways to measure uncertainty are tested, and one way (called Jensen-Shannon Divergence) is found to be the best for helping the agent learn. This study suggests that there are three important parts to making this type of learning work: helping the agent move around while it learns, teaching the agent what it can do in its environment, and using special techniques to figure out where the agent will learn the most. |
Keywords
» Artificial intelligence » Active learning » Deep learning
