言語誘導型適応知覚のための階層的記号表現による移動マニピュレータの環境モデリング
Language-guided Adaptive Perception with Hierarchical Symbolic Representations for Mobile Manipulators
言語指示に応じて物体検出器の階層構造を適応的に選択・表現する記号表現手法を提案し、移動マニピュレータの実機実験で網羅的ベースラインと知覚性能・環境表現を比較した。
著者: Ethan Fahnestock, Siddharth Patki, Thomas M. Howard
分類: cs.RO
原文アブストラクト
Language is an effective medium for bi-directional communication in human-robot teams. To infer the meaning of many instructions, robots need to construct a model of their surroundings that describe the spatial, semantic, and metric properties of objects from observations and prior information about the environment. Recent algorithms condition the expression of object detectors in a robot's perception pipeline on language to generate a minimal representation of the environment necessary to efficiently determine the meaning of the instruction. We expand on this work by introducing the ability to express hierarchies between detectors. This assists in the development of environment models suitable for more sophisticated tasks that may require modeling of kinematics, dynamics, and/or affordances between objects. To achieve this, a novel extension of symbolic representations for language-guided adaptive perception is proposed that reasons over single-layer object detector hierarchies. Differences in perception performance and environment representations between adaptive perception and a suitable exhaustive baseline are explored through physical experiments on a mobile manipulator.
