Curiosity-driven learning for physically grounded autonomous agents
Abstract/Contents
- Abstract
- The human ability to solve complex manipulation tasks is based on a flexible generalizable understanding of intuitive physics mostly learned through curiosity-driven self-play during infancy. We aim to replicate such interactive learning in artificial agents to achieve the same flexibility and generalizability when solving complex manipulation tasks. For that purpose, we introduce a general framework for learning intuitive physics through curiosity-driven self-play for artificial agents. Within this framework, we demonstrate how object-centric representations can greatly improve intuitive physics predictions and support stochastic predictions of complex physical scenes modeling uncertainty, and then show that object-centric physics prediction models can be trained within the presented curiosity-driven framework. Lastly, we apply our findings to drive the exploration of robotic systems to advance the generalizability of manipulation policies for complex pick and place tasks, and to measure and model human intuitive physics on a wide variety of visual stimuli of complex physics scenarios.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Mrowca, Damian |
|---|---|
| Degree supervisor | Li, Fei Fei, 1976- |
| Degree supervisor | Yamins, Daniel |
| Thesis advisor | Li, Fei Fei, 1976- |
| Thesis advisor | Yamins, Daniel |
| Thesis advisor | Haber, Nick |
| Thesis advisor | Savarese, Silvio |
| Degree committee member | Haber, Nick |
| Degree committee member | Savarese, Silvio |
| Associated with | Stanford University, Computer Science Department |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Damian Mrowca. |
|---|---|
| Note | Submitted to the Computer Science Department. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/gw743pk3869 |
Access conditions
- Copyright
- © 2021 by Damian Mrowca
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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