1,289 research outputs found
A Review of Verbal and Non-Verbal Human-Robot Interactive Communication
In this paper, an overview of human-robot interactive communication is
presented, covering verbal as well as non-verbal aspects of human-robot
interaction. Following a historical introduction, and motivation towards fluid
human-robot communication, ten desiderata are proposed, which provide an
organizational axis both of recent as well as of future research on human-robot
communication. Then, the ten desiderata are examined in detail, culminating to
a unifying discussion, and a forward-looking conclusion
Conceptual spatial representations for indoor mobile robots
We present an approach for creating conceptual representations of human-made indoor environments using mobile
robots. The concepts refer to spatial and functional properties of typical indoor environments. Following ļ¬ndings
in cognitive psychology, our model is composed of layers representing maps at diļ¬erent levels of abstraction. The
complete system is integrated in a mobile robot endowed with laser and vision sensors for place and object recognition.
The system also incorporates a linguistic framework that actively supports the map acquisition process, and which
is used for situated dialogue. Finally, we discuss the capabilities of the integrated system
Learning to Generate Unambiguous Spatial Referring Expressions for Real-World Environments
Referring to objects in a natural and unambiguous manner is crucial for
effective human-robot interaction. Previous research on learning-based
referring expressions has focused primarily on comprehension tasks, while
generating referring expressions is still mostly limited to rule-based methods.
In this work, we propose a two-stage approach that relies on deep learning for
estimating spatial relations to describe an object naturally and unambiguously
with a referring expression. We compare our method to the state of the art
algorithm in ambiguous environments (e.g., environments that include very
similar objects with similar relationships). We show that our method generates
referring expressions that people find to be more accurate (30% better)
and would prefer to use (32% more often).Comment: International Conference on Intelligent Robots and Systems (IROS
2019), Demo 1: Finding the described object (https://youtu.be/BE6-F6chW0w),
Demo 2: Referring to the pointed object (https://youtu.be/nmmv6JUpy8M),
Supplementary Video (https://youtu.be/sFjBa_MHS98
Reference and the facilitation of search in spatial domains
This is a pre-final version of the article, whose official publication is expected in the winter of 2013-14.Peer reviewedPreprin
From Verbs to Tasks: An Integrated Account of Learning Tasks from Situated Interactive Instruction.
Intelligent collaborative agents are becoming common in the human society. From virtual assistants such as Siri and Google Now to assistive robots, they contribute to human activities in a variety of ways. As they become more pervasive, the challenge of customizing them to a variety of environments and tasks becomes critical. It is infeasible for engineers to program them for each individual use. Our research aims at building interactive robots and agents that adapt to new environments autonomously by interacting with human users using natural modalities.
This dissertation studies the problem of learning novel tasks from human-agent dialog. We propose a novel approach for interactive task learning, situated interactive instruction (SII), and investigate approaches to three computational challenges that arise in designing SII agents: situated comprehension, mixed-initiative interaction, and interactive task learning. We propose a novel mixed-modality grounded representation for task verbs which encompasses their lexical, semantic, and
task-oriented aspects. This representation is useful in situated comprehension and can be learned through human-agent interactions. We introduce the Indexical Model of comprehension that can exploit
extra-linguistic contexts for resolving semantic ambiguities in situated comprehension of task commands. The Indexical model is integrated with a mixed-initiative interaction model that facilitates
a flexible task-oriented human-agent dialog. This dialog serves as the basis of interactive task learning. We propose an interactive variation of explanation-based learning that can acquire the proposed
representation. We demonstrate that our learning paradigm is efficient, can transfer knowledge between structurally similar tasks, integrates agent-driven exploration with instructional learning, and can acquire several tasks. The methods proposed in this thesis are integrated in Rosie - a generally instructable agent developed in the Soar cognitive architecture and embodied on a table-top robot.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111573/1/shiwali_1.pd
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