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Title: Language-guided Semantic Mapping and Mobile Manipulation in Partially Observable Environments
Recent advances in data-driven models for grounded language understanding have enabled robots to interpret increasingly complex instructions. Two fundamental limitations of these methods are that most require a full model of the environment to be known a priori, and they attempt to reason over a world representation that is flat and unnecessarily detailed, which limits scalability. Recent semantic mapping methods address partial observability by exploiting language as a sensor to infer a distribution over topological, metric and semantic properties of the environment. However, maintaining a distribution over highly detailed maps that can support grounding of diverse instructions is computationally expensive and hinders real-time human-robot collaboration. We propose a novel framework that learns to adapt perception according to the task in order to maintain compact distributions over semantic maps. Experiments with a mobile manipulator demonstrate more efficient instruction following in a priori unknown environments.
Authors:
; ; ;
Award ID(s):
1637813
Publication Date:
NSF-PAR ID:
10195684
Journal Name:
Proceedings of Machine Learning Research
Volume:
100
Page Range or eLocation-ID:
1201-1210
ISSN:
2640-3498
Sponsoring Org:
National Science Foundation
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