Reasoning about physical interactions with object-oriented prediction and planning
Object-based factorizations provide a useful level of abstraction for interacting with the world. Building explicit object representations, however, often requires supervisory signals that are difficult to obtain in practice. We present a paradigm for learning object-centric representations for phys...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
International Conference on Learning Representations
2020
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| Online Access: | https://hdl.handle.net/1721.1/126589 |