On Grounded Planning for Embodied Tasks with Language Models
نویسندگان
چکیده
Language models (LMs) have demonstrated their capability in possessing commonsense knowledge of the physical world, a crucial aspect performing tasks everyday life. However, it remains unclear whether they capacity to generate grounded, executable plans for embodied tasks. This is challenging task as LMs lack ability perceive environment through vision and feedback from environment. In this paper, we address important research question present first investigation into topic. Our novel problem formulation, named G-PlanET, inputs high-level goal data table about objects specific environment, then outputs step-by-step actionable plan robotic agent follow. To facilitate study, establish an evaluation protocol design dedicated metric, KAS, assess quality plans. experiments demonstrate that use tables encoding iterative decoding strategy can significantly enhance LMs' grounded planning. analysis also reveals interesting non-trivial findings.
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ژورنال
عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence
سال: 2023
ISSN: ['2159-5399', '2374-3468']
DOI: https://doi.org/10.1609/aaai.v37i11.26549