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Aravind Elanjimattathil Vijayan

dblp:226/6428 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2018
0000-0002-0626-5021ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 first-authorSystems, architecture and hardware · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
motion retargeting
0.312018
Using Constrained Optimization for Real-Time Synchronization of Verbal and Nonverbal Robot Behavior · ICRA 2018
Human-robot interaction › nonverbal communication
nonverbal robot behavior
0.312018
Using Constrained Optimization for Real-Time Synchronization of Verbal and Nonverbal Robot Behavior · ICRA 2018

Methods — techniques the papers use, named apart from their topics

quadratic programming · 0.3constrained optimization · 0.3
YearPublicationVenuePosition
2018 Using Constrained Optimization for Real-Time Synchronization of Verbal and Nonverbal Robot Behavior
abstract
Most of the motion re-targeting techniques are grounded on virtual character animation research, which means that they typically assume that the target embodiment has unconstrained joint angular velocities. However, because robots often do have such constraints, traditional re-targeting approaches can originate irregular delays in the robot motion. With the goal of ensuring synchronization between verbal and nonverbal behavior, this paper proposes an optimization framework for processing re-targeted motion sequences that addresses constraints such as joint angle and angular velocities. The proposed framework was evaluated on a humanoid robot using both objective and subjective metrics. While the analysis of the joint motion trajectories provides evidence that our framework successfully performs the desired modifications to ensure verbal and nonverbal behavior synchronization, results from a perceptual study showed that participants found the robot motion generated by our method more natural, elegant and lifelike than a control condition.
Aravind Elanjimattathil Vijayan, Simon Alexanderson, Jonas Beskow, Iolanda Leite
ICRA1