Related papers: Learning Planning Abstractions from Language

Learning Planning Abstractions from Language

URL: http://arxiv.org/abs/2405.03864v1
Date: Mon, 6 May 2024 21:24:22 GMT
Title: Learning Planning Abstractions from Language
Authors: Weiyu Liu, Geng Chen, Joy Hsu, Jiayuan Mao, Jiajun Wu,
Abstract summary: This paper presents a framework for learning state and action abstractions in sequential decision-making domains. Our framework, planning abstraction from language (PARL), utilizes language-annotated demonstrations to automatically discover a symbolic and abstract action space.
Score: 28.855381137615275
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This paper presents a framework for learning state and action abstractions in sequential decision-making domains. Our framework, planning abstraction from language (PARL), utilizes language-annotated demonstrations to automatically discover a symbolic and abstract action space and induce a latent state abstraction based on it. PARL consists of three stages: 1) recovering object-level and action concepts, 2) learning state abstractions, abstract action feasibility, and transition models, and 3) applying low-level policies for abstract actions. During inference, given the task description, PARL first makes abstract action plans using the latent transition and feasibility functions, then refines the high-level plan using low-level policies. PARL generalizes across scenarios involving novel object instances and environments, unseen concept compositions, and tasks that require longer planning horizons than settings it is trained on.

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