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# Default owners for all files. | ||
* @nkumar-bdai @wmcclinton @tsilver-bdai | ||
* @NishanthJKumar @wmcclinton @tsilver-bdai |
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## Repository Description | ||
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This codebase implements a framework for *bilevel planning with learned neuro-symbolic relational abstractions*, as described in [this paper](https://arxiv.org/abs/2203.09634). Several features are concurrently under active development. **Please contact <[email protected]> or <[email protected]> before attempting to use it for your own research.** In particular, this codebase aims to ultimately provide an integrated system for learning the ingredients of search-then-sample bilevel planning with learned abstractions. That includes: options, predicates, operators, and samplers. | ||
This codebase implements a framework for *bilevel planning with learned neuro-symbolic relational abstractions*, as described in the following papers: | ||
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1. [Learning Symbolic Operators for Task and Motion Planning](https://arxiv.org/abs/2103.00589). Silver*, Chitnis*, Tenenbaum, Kaelbling, Lozano-Perez. IROS 2021. | ||
2. [Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning](https://arxiv.org/abs/2105.14074). Chitnis*, Silver*, Tenenbaum, Lozano-Perez, Kaelbling. IROS 2022. | ||
3. [Learning Neuro-Symbolic Skills for Bilevel Planning](http://arxiv.org/abs/2206.10680). Silver, Athalye, Tenenbaum, Lozano-Perez, Kaelbling. CoRL 2022. | ||
4. [Predicate Invention for Bilevel Planning](https://arxiv.org/abs/2203.09634). Silver*, Chitnis*, Kumar, McClinton, Lozano-Perez, Kaelbling, Tenenbaum. AAAI 2023. | ||
5. [Embodied Active Learning of Relational State Abstractions for Bilevel Planning](https://arxiv.org/abs/2303.04912). Li, Silver. CoLLAs 2023. | ||
6. [Learning Efficient Abstract Planning Models that Choose What to Predict](https://arxiv.org/abs/2208.07737). Kumar*, McClinton*, Chitnis, Silver, Lozano-Perez, Kaelbling. CoRL 2023. | ||
7. [Practice Makes Perfect: Planning to Learn Skill Parameter Policies](https://arxiv.org/abs/2402.15025). Kumar*, Silver*, McClinton, Zhao, Proulx, Lozano-Perez, Kaelbling, Barry. Under Review 2024. | ||
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The codebase is still under active development. **Please contact <[email protected]> or <[email protected]> before attempting to use it for your own research.** | ||
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### Code Structure | ||
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A simple implementation of search-then-sample bilevel planning is provided in `predicators/planning.py`. This implementation uses the "SeSamE" strategy: SEarch-and-SAMple planning, then Execution. | ||
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## Installation | ||
* This repository uses Python versions 3.8+. | ||
* This repository uses Python versions 3.10-3.11. We recommend 3.10.14. | ||
* Run `pip install -e .` to install dependencies. | ||
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## Instructions For Running Code | ||
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