Summary of Few-shot Task Learning Through Inverse Generative Modeling, by Aviv Netanyahu et al.
Few-Shot Task Learning through Inverse Generative Modeling
by Aviv Netanyahu, Yilun Du, Antonia Bronars, Jyothish Pari, Joshua Tenenbaum, Tianmin Shu, Pulkit Agrawal
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 Few-Shot Task Learning through Inverse Generative Modeling (FTL-IGM) approach learns new task concepts by leveraging invertible neural generative models. It pretrains a generative model on basic concepts and their demonstrations, then updates the underlying concepts without modifying the model weights using backpropagation. The method is evaluated in five domains: object rearrangement, goal-oriented navigation, motion caption of human actions, autonomous driving, and real-world table-top manipulation. Results show that FTL-IGM successfully learns novel concepts and generates agent plans or motion corresponding to these concepts in unseen environments and composition with training concepts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn new tasks from just a few examples by using special models called generative models. These models are pre-trained on basic concepts, then use those basics to learn new ideas without having to retrain the entire model. This is tested in five different areas: moving objects around, getting to a goal, describing human actions, self-driving cars, and playing with toys. The results show that this method can successfully teach computers new things and make them work together with what they already know. |
Keywords
» Artificial intelligence » Backpropagation » Few shot » Generative model
