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Khurram Gulzar

dblp:72/11349 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
0000-0003-4412-2723ORCID · reported

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

Applied, interdisciplinary, general and emerging computing · 1

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.

Artificial intelligence
1 paper
Multi-agent systems · 100%
Human-computer interaction and pervasive computing
1 paper
Interaction techniques and input · 50% Human-robot interaction · 50%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.412019
Robot-Robot Gesturing for Anchoring Representations · IEEE Trans. Robotics 2019
Human-robot interaction › multi-robot systems
heterogeneous robot teams
0.112019
Robot-Robot Gesturing for Anchoring Representations · IEEE Trans. Robotics 2019
Interaction techniques and input › gesture input
pointing gesture
0.112019
Robot-Robot Gesturing for Anchoring Representations · IEEE Trans. Robotics 2019

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

simulation · 0.8decentralized planning · 0.8
YearPublicationVenuePosition
2019 Robot-Robot Gesturing for Anchoring Representations
abstract
In a multirobot system, using shared symbols for objects in the environment is a prerequisite for collaboration. Sharing symbols requires that each agent has anchored a symbol with an internal, sensor level representation, as well as that these symbols match between the agents. The problem can be solved easily when the internal representations can be communicated between the agents. However, with heterogeneous embodiments the available sensors are likely to differ, making it impossible to share the internal representations directly. We propose the use of pointing gestures to align symbols between a heterogeneous group of robots. We describe a planning framework that minimizes the required effort for anchoring representations across robots. The framework allows planning for both the gesturing and observing agents in a decentralized fashion. It considers both implicit sources of failure, such as ambiguous pointing, as well as costs required by actions. Simulation experiments demonstrate that the resulting planning problem has a complex solution structure with multiple local minima. Demonstration with a heterogeneous two-robot system shows the practical viability of this approach.
Polychronis Kondaxakis, Khurram Gulzar, Stefan Kinauer, Iasonas Kokkinos, Ville Kyrki
IEEE Trans. Robotics2