Summary of Incremental Learning Of Affordances Using Markov Logic Networks, by George Potter et al.
Incremental Learning of Affordances using Markov Logic Networks
by George Potter, Gertjan Burghouts, Joris Sijs
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 This paper explores how robots can develop a deeper understanding of their environment by capturing object “affordances” using Markov Logic Networks (MLNs). Affordances allow robots to adapt to changing situations while completing tasks. The challenge lies in incorporating new contextual information into the MLN model without retraining it from scratch. To address this, the authors introduce the MLN Cumulative Learning Algorithm (MLN-CLA), which learns new relationships by retaining existing knowledge and updating only what’s changed. Results show that MLN-CLA outperforms strong baselines in both cumulative learning and zero-shot affordance inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping robots understand their world better. It’s like when you learn something new, but then you forget some of the old things. The robots have to remember what they already know and add new information without starting from scratch. They use special computer models called Markov Logic Networks (MLNs) that can do this. The authors made a new way for these models to learn, which is better than other ways. |
Keywords
* Artificial intelligence * Inference * Zero shot
