title: Detecting Target Objects by Natural Language Instructions Using an RGB-D Camera.
abstract: Controlling robots by natural language (NL) is increasingly attracting attention for its versatility, convenience and no need of extensive training for users. Grounding is a crucial challenge of this problem to enable robots to understand NL instructions from humans. This paper mainly explores the object grounding problem and concretely studies how to detect target objects by the NL instructions using an RGB-D camera in robotic manipulation applications. In particular, a simple yet robust vision algorithm is applied to segment objects of interest. With the metric information of all segmented objects, the object attributes and relations between objects are further extracted. The NL instructions that incorporate multiple cues for object specifications are parsed into domain-specific annotations. The annotations from NL and extracted information from the RGB-D camera are matched in a computational state estimation framework to search all possible object grounding states. The final grounding is accomplished by selecting the states which have the maximum probabilities. An RGB-D scene dataset associated with different groups of NL instructions based on different cognition levels of the robot are collected. Quantitative evaluations on the dataset illustrate the advantages of the proposed method. The experiments of NL controlled object manipulation and NL-based task programming using a mobile manipulator show its effectiveness and practicability in robotic applications.
authors.affiliation: Department of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225000, China. jtbao@yzu.edu.cn.
authors.forename: Jiatong
authors.initials: J
authors.lastname: Bao
authors.affiliation: Department of Automotive Engineering, Clemson University, Greenville, SC 29607, USA. yunyij@clemson.edu.
authors.forename: Yunyi
authors.initials: Y
authors.lastname: Jia
authors.affiliation: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA. chengyu9@msu.edu.
authors.forename: Yu
authors.initials: Y
authors.lastname: Cheng
authors.affiliation: Department of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou 225000, China. hrtang@yzu.edu.cn.
authors.forename: Hongru
authors.initials: H
authors.lastname: Tang
authors.affiliation: Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA. xin@msu.edu.
authors.forename: Ning
authors.initials: N
authors.lastname: Xi
keywords: natural language control; natural language processing; object grounding; object recognition; robotic manipulation system; target object detection