3,065 research outputs found
Motion Imitation Based on Sparsely Sampled Correspondence
Existing techniques for motion imitation often suffer a certain level of
latency due to their computational overhead or a large set of correspondence
samples to search. To achieve real-time imitation with small latency, we
present a framework in this paper to reconstruct motion on humanoids based on
sparsely sampled correspondence. The imitation problem is formulated as finding
the projection of a point from the configuration space of a human's poses into
the configuration space of a humanoid. An optimal projection is defined as the
one that minimizes a back-projected deviation among a group of candidates,
which can be determined in a very efficient way. Benefited from this
formulation, effective projections can be obtained by using sparse
correspondence. Methods for generating these sparse correspondence samples have
also been introduced. Our method is evaluated by applying the human's motion
captured by a RGB-D sensor to a humanoid in real-time. Continuous motion can be
realized and used in the example application of tele-operation.Comment: 8 pages, 8 figures, technical repor
The perception of emotion in artificial agents
Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents
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
A Posture Sequence Learning System for an Anthropomorphic Robotic Hand
The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator
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