Inventing Relational State and Action Abstractions for Effective and
Efficient Bilevel Planning
- URL: http://arxiv.org/abs/2203.09634v1
- Date: Thu, 17 Mar 2022 22:13:09 GMT
- Title: Inventing Relational State and Action Abstractions for Effective and
Efficient Bilevel Planning
- Authors: Tom Silver, Rohan Chitnis, Nishanth Kumar, Willie McClinton, Tomas
Lozano-Perez, Leslie Pack Kaelbling, Joshua Tenenbaum
- Abstract summary: We develop a novel framework for learning state and action abstractions.
We learn relational, neuro-symbolic abstractions that generalize over object identities and numbers.
We show that our learned abstractions are able to quickly solve held-out tasks of longer horizons.
- Score: 26.715198108255162
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Effective and efficient planning in continuous state and action spaces is
fundamentally hard, even when the transition model is deterministic and known.
One way to alleviate this challenge is to perform bilevel planning with
abstractions, where a high-level search for abstract plans is used to guide
planning in the original transition space. In this paper, we develop a novel
framework for learning state and action abstractions that are explicitly
optimized for both effective (successful) and efficient (fast) bilevel
planning. Given demonstrations of tasks in an environment, our data-efficient
approach learns relational, neuro-symbolic abstractions that generalize over
object identities and numbers. The symbolic components resemble the STRIPS
predicates and operators found in AI planning, and the neural components refine
the abstractions into actions that can be executed in the environment.
Experimentally, we show across four robotic planning environments that our
learned abstractions are able to quickly solve held-out tasks of longer
horizons than were seen in the demonstrations, and can even outperform the
efficiency of abstractions that we manually specified. We also find that as the
planner configuration varies, the learned abstractions adapt accordingly,
indicating that our abstraction learning method is both "task-aware" and
"planner-aware." Code: https://tinyurl.com/predicators-release
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