Web: http://arxiv.org/abs/2209.09874

Sept. 21, 2022, 1:13 a.m. | Boyuan Chen, Fei Xia, Brian Ichter, Kanishka Rao, Keerthana Gopalakrishnan, Michael S. Ryoo, Austin Stone, Daniel Kappler

cs.CV updates on arXiv.org arxiv.org

Large language models (LLMs) have unlocked new capabilities of task planning
from human instructions. However, prior attempts to apply LLMs to real-world
robotic tasks are limited by the lack of grounding in the surrounding scene. In
this paper, we develop NLMap, an open-vocabulary and queryable scene
representation to address this problem. NLMap serves as a framework to gather
and integrate contextual information into LLM planners, allowing them to see
and query available objects in the scene before generating a
context-conditioned …

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