13,365 research outputs found
Symbol Emergence in Robotics: A Survey
Humans can learn the use of language through physical interaction with their
environment and semiotic communication with other people. It is very important
to obtain a computational understanding of how humans can form a symbol system
and obtain semiotic skills through their autonomous mental development.
Recently, many studies have been conducted on the construction of robotic
systems and machine-learning methods that can learn the use of language through
embodied multimodal interaction with their environment and other systems.
Understanding human social interactions and developing a robot that can
smoothly communicate with human users in the long term, requires an
understanding of the dynamics of symbol systems and is crucially important. The
embodied cognition and social interaction of participants gradually change a
symbol system in a constructive manner. In this paper, we introduce a field of
research called symbol emergence in robotics (SER). SER is a constructive
approach towards an emergent symbol system. The emergent symbol system is
socially self-organized through both semiotic communications and physical
interactions with autonomous cognitive developmental agents, i.e., humans and
developmental robots. Specifically, we describe some state-of-art research
topics concerning SER, e.g., multimodal categorization, word discovery, and a
double articulation analysis, that enable a robot to obtain words and their
embodied meanings from raw sensory--motor information, including visual
information, haptic information, auditory information, and acoustic speech
signals, in a totally unsupervised manner. Finally, we suggest future
directions of research in SER.Comment: submitted to Advanced Robotic
Enactivism and Robotic Language Acquisition: A Report from the Frontier
In this article, I assess an existing language acquisition architecture, which was deployed in linguistically unconstrained human–robot interaction, together with experimental design decisions with regard to their enactivist credentials. Despite initial scepticism with respect to enactivism’s applicability to the social domain, the introduction of the notion of participatory sense-making in the more recent enactive literature extends the framework’s reach to encompass this domain. With some exceptions, both our architecture and form of experimentation appear to be largely compatible with enactivist tenets. I analyse the architecture and design decisions along the five enactivist core themes of autonomy, embodiment, emergence, sense-making, and experience, and discuss the role of affect due to its central role within our acquisition experiments. In conclusion, I join some enactivists in demanding that interaction is taken seriously as an irreducible and independent subject of scientific investigation, and go further by hypothesising its potential value to machine learning.Peer reviewedFinal Published versio
Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions
Comprehension of spoken natural language is an essential component for robots
to communicate with human effectively. However, handling unconstrained spoken
instructions is challenging due to (1) complex structures including a wide
variety of expressions used in spoken language and (2) inherent ambiguity in
interpretation of human instructions. In this paper, we propose the first
comprehensive system that can handle unconstrained spoken language and is able
to effectively resolve ambiguity in spoken instructions. Specifically, we
integrate deep-learning-based object detection together with natural language
processing technologies to handle unconstrained spoken instructions, and
propose a method for robots to resolve instruction ambiguity through dialogue.
Through our experiments on both a simulated environment as well as a physical
industrial robot arm, we demonstrate the ability of our system to understand
natural instructions from human operators effectively, and how higher success
rates of the object picking task can be achieved through an interactive
clarification process.Comment: 9 pages. International Conference on Robotics and Automation (ICRA)
2018. Accompanying videos are available at the following links:
https://youtu.be/_Uyv1XIUqhk (the system submitted to ICRA-2018) and
http://youtu.be/DGJazkyw0Ws (with improvements after ICRA-2018 submission
Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction
Recognition of social signals, from human facial expressions or prosody of
speech, is a popular research topic in human-robot interaction studies. There
is also a long line of research in the spoken dialogue community that
investigates user satisfaction in relation to dialogue characteristics.
However, very little research relates a combination of multimodal social
signals and language features detected during spoken face-to-face human-robot
interaction to the resulting user perception of a robot. In this paper we show
how different emotional facial expressions of human users, in combination with
prosodic characteristics of human speech and features of human-robot dialogue,
correlate with users' impressions of the robot after a conversation. We find
that happiness in the user's recognised facial expression strongly correlates
with likeability of a robot, while dialogue-related features (such as number of
human turns or number of sentences per robot utterance) correlate with
perceiving a robot as intelligent. In addition, we show that facial expression,
emotional features, and prosody are better predictors of human ratings related
to perceived robot likeability and anthropomorphism, while linguistic and
non-linguistic features more often predict perceived robot intelligence and
interpretability. As such, these characteristics may in future be used as an
online reward signal for in-situ Reinforcement Learning based adaptive
human-robot dialogue systems.Comment: Robo-NLP workshop at ACL 2017. 9 pages, 5 figures, 6 table
Exploiting Deep Semantics and Compositionality of Natural Language for Human-Robot-Interaction
We develop a natural language interface for human robot interaction that
implements reasoning about deep semantics in natural language. To realize the
required deep analysis, we employ methods from cognitive linguistics, namely
the modular and compositional framework of Embodied Construction Grammar (ECG)
[Feldman, 2009]. Using ECG, robots are able to solve fine-grained reference
resolution problems and other issues related to deep semantics and
compositionality of natural language. This also includes verbal interaction
with humans to clarify commands and queries that are too ambiguous to be
executed safely. We implement our NLU framework as a ROS package and present
proof-of-concept scenarios with different robots, as well as a survey on the
state of the art
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