7,981 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 and Reasoning for Robot Sequential Decision Making under Uncertainty
Robots frequently face complex tasks that require more than one action, where
sequential decision-making (SDM) capabilities become necessary. The key
contribution of this work is a robot SDM framework, called LCORPP, that
supports the simultaneous capabilities of supervised learning for passive state
estimation, automated reasoning with declarative human knowledge, and planning
under uncertainty toward achieving long-term goals. In particular, we use a
hybrid reasoning paradigm to refine the state estimator, and provide
informative priors for the probabilistic planner. In experiments, a mobile
robot is tasked with estimating human intentions using their motion
trajectories, declarative contextual knowledge, and human-robot interaction
(dialog-based and motion-based). Results suggest that, in efficiency and
accuracy, our framework performs better than its no-learning and no-reasoning
counterparts in office environment.Comment: In proceedings of 34th AAAI conference on Artificial Intelligence,
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Interactive semantic mapping: Experimental evaluation
Robots that are launched in the consumer market need to provide more effective human robot interaction, and, in particular, spoken language interfaces. However, in order to support the execution of high level commands as they are specified in natural language, a semantic map is required. Such a map is a representation that enables the robot to ground the commands into the actual places and objects located in the environment. In this paper, we present the experimental evaluation of a system specifically designed to build semantically rich maps, through the interaction with the user. The results of the experiments not only provide the basis for a discussion of the features of the proposed approach, but also highlight the manifold issues that arise in the evaluation of semantic mapping
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