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.
Highlights
As assistants to human beings, robots are moving into more service oriented roles in human life, both in living and working
For the action grounding—to transfer the actions described in the natural language (NL) to some defined robot actions—A set of mapping rules could be predefined or learned, since actions considered for Sensors 2016, 16, 2117; doi:10.3390/s16122117
The contribution of this paper is three-fold: (i) we formulate the problem of NL-based target object detection as the state estimation in the space of all possible object grounding states according to visual object segmentation results and extracted linguistic object cues; (ii) an RGB-D scene dataset as well as different groups of NL instructions based on different cognition levels of the robot are collected for evaluation of target object detection in robotic manipulation applications; and (iii) we show quantitative evaluation results on the dataset and experimentally validate the effectiveness and practicability of the proposed method on the applications of NL controlled object manipulation and NL-based task programming using our mobile manipulator system
Summary
As assistants to human beings, robots are moving into more service oriented roles in human life, both in living and working. This work mainly explores the object grounding problem and concretely studies how to detect target objects by NL instructions using an RGB-D camera in robotic manipulation tasks. The contribution of this paper is three-fold: (i) we formulate the problem of NL-based target object detection as the state estimation in the space of all possible object grounding states according to visual object segmentation results and extracted linguistic object cues; (ii) an RGB-D scene dataset as well as different groups of NL instructions based on different cognition levels of the robot are collected for evaluation of target object detection in robotic manipulation applications; and (iii) we show quantitative evaluation results on the dataset and experimentally validate the effectiveness and practicability of the proposed method on the applications of NL controlled object manipulation and NL-based task programming using our mobile manipulator system.
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