A Bayesian Approach to Phrase Understanding through Cross-Situational Learning (original) (raw)
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IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids) Beijing, China, November 6-9, 2018, 2018
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Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Australia, 2018
Creating successful human-robot collaboration requires robots to have high-level cognitive functions that could allow them to understand human language and actions in space. To meet this target, an elusive challenge that we address in this paper is to understand object-directed actions through grounding language based on visual cues representing the dynamics of human actions on objects, object characteristics (color and geometry), and spatial relationships between objects in a tabletop scene. The proposed probabilistic framework investigates unsupervised Part-of-Speech (POS) tagging to determine syntactic categories of words so as to infer grammatical structure of language. The dynamics of object-directed actions are characterized through the locations of the human arm joints-modeled on a Hidden Markov Model (HMM)-while manipulating objects, in addition to those of objects represented in 3D point clouds. These corresponding point clouds to segmented objects encode geometric features and spatial semantics of referents and landmarks in the environment. The proposed Bayesian learning model is successfully evaluated through interaction experiments between a human user and Toyota HSR robot in space.
2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2010
Language is among the most fascinating and complex cognitive activities that develops rapidly since the early months of infants' life. The aim of the present work is to provide a humanoid robot with cognitive, perceptual and motor skills fundamental for the acquisition of a rudimentary form of language. We present a novel probabilistic model, inspired by the findings in cognitive sciences, able to associate spoken words with their perceptually grounded meanings. The main focus is set on acquiring the meaning of various perceptual categories (e.g. red, blue, circle, above, etc.), rather than specific world entities (e.g. an apple, a toy, etc.). Our probabilistic model is based on a variant of multi-instance learning technique, and it enables a robotic platform to learn grounded meanings of adjective/noun terms. The systems could be used to understand and generate appropriate natural language descriptions of real objects in a scene, and it has been successfully tested on the NAO humanoid robotic platform.
Natural Language Acquisition and Grounding for Embodied Robotic Systems
Proceedings of the AAAI Conference on Artificial Intelligence
We present a cognitively plausible novel framework capable of learning the grounding in visual semantics and the grammar of natural language commands given to a robot in a table top environment. The input to the system consists of video clips of a manually controlled robot arm, paired with natural language commands describing the action. No prior knowledge is assumed about the meaning of words, or the structure of the language, except that there are different classes of words (corresponding to observable actions, spatial relations, and objects and their observable properties). The learning process automatically clusters the continuous perceptual spaces into concepts corresponding to linguistic input. A novel relational graph representation is used to build connections between language and vision. As well as the grounding of language to perception, the system also induces a set of probabilistic grammar rules. The knowledge learned is used to parse new commands involving previously un...
Natural Language Grounding and Grammar Induction for Robotic Manipulation Commands
2017
We present a cognitively plausible system capable of acquiring knowledge in language and vision from pairs of short video clips and linguistic descriptions. The aim of this work is to teach a robot manipulator how to execute natural language commands by demonstration. This is achieved by first learning a set of visual 'concepts' that abstract the visual feature spaces into concepts that have human-level meaning. Second, learning the mapping/grounding between words and the extracted visual concepts. Third, inducing grammar rules via a semantic representation known as Robot Control Language (RCL). We evaluate our approach against state-of-the-art supervised and unsupervised grounding and grammar induction systems, and show that a robot can learn to execute never seenbefore commands from pairs of unlabelled linguistic and visual inputs.
Grounding language in perception for scene conceptualization in autonomous robots
In order to behave autonomously, it is desirable for robots to have the ability to use human supervision and learn from different input sources (perception, gestures, verbal and textual descriptions etc). In many machine learning tasks, the supervision is directed specifically towards machines and hence is straight forward clearly annotated examples. But this is not always very practical and recently it was found that the most preferred interface to robots is natural language. Also the supervision might only be available in a rather indirect form, which may be vague and incomplete. This is frequently the case when humans teach other humans since they may assume a particular context and existing world knowledge. We explore this idea here in the setting of conceptualizing objects and scene layouts. Initially the robot undergoes training from a human in recognizing some objects in the world and armed with this acquired knowledge it sets out in the world to explore and learn more higher level concepts like static scene layouts and environment activities. Here it has to exploit its learned knowledge and ground language into perception to use inputs from different sources that might have overlapping as well as novel information. When exploring, we assume that the robot is given visual input, without explicit type labels for objects, and also that it has access to more or less generic linguistic descriptions of scene layout. Thus our task here is to learn the spatial structure of a scene layout and simultaneously visual object models it was not trained on. In this paper, we present a cognitive architecture and learning framework for robot learning through natural human supervision and using multiple input sources by grounding language in perception.
Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations
Proceedings of the Combined Workshop on Spatial Language Understanding (, 2019
Human-robot interaction often occurs in the form of instructions given from a human to a robot. For a robot to successfully follow instructions, a common representation of the world and objects in it should be shared between humans and the robot so that the instructions can be grounded. Achieving this representation can be done via learning, where both the world representation and the language grounding are learned simultaneously. However, in robotics this can be a difficult task due to the cost and scarcity of data. In this paper, we tackle the problem by separately learning the world representation of the robot and the language grounding. While this approach can address the challenges in getting sufficient data, it may give rise to inconsistencies between both learned components. Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot's world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human. Moreover, we demonstrate the feasibility of our approach on a scenario involving a robotic arm in the physical world.
Proceedings of the 7th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics (ICDL-EPIROB), Portugal, 2017
Future human-robot collaboration employs language in instructing a robot about specific tasks to perform in its surroundings. This requires the robot to be able to associate spatial knowledge with language to understand the details of an assigned task so as to behave appropriately in the context of interaction. In this paper, we propose a probabilistic framework for learning the meaning of language spatial concepts (spatial prepositions) and object categories based on visual cues representing spatial layouts and geometric characteristics of objects in a tabletop scene. The model investigates unsupervised Part-of-Speech (POS) tagging through a Hidden Markov Model (HMM) that infers the corresponding hidden tags to words. Spatial configurations and geometric characteristics of objects on the tabletop are described through 3D point cloud information that encodes spatial semantics and categories of referents and landmarks in the environment. The proposed model is evaluated through human user interaction with Toyota HSR robot, where the obtained results show the significant effect of the model in making the robot able to successfully engage in interaction with the user in space.
Proceedings of the 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2019
Abstract—In order to interact with people in a natural way, a robot must be able to link words to objects and actions. Although previous studies in the literature have investigated grounding, they did not consider grounding of unknown synonyms. In this paper, we introduce a probabilistic model for grounding unknown synonymous object and action names using cross-situational learning. The proposed Bayesian learning model uses four different word representations to determine synonymous words. Afterwards, they are grounded through geometric characteristics of objects and kinematic features of the robot joints during action execution. The proposed model is evaluated through an interaction experiment between a human tutor and HSR robot. The results show that semantic and syntactic information both enable grounding of unknown synonyms and that the combination of both achieves the best grounding.