Zhuoli Tian

dblp:378/1560 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

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

Artificial intelligence and machine learning · 1 · 1 since 2021Systems, architecture and hardware · 1 · 1 since 2021

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
Reinforcement learning · 50% Multi-agent systems · 50%
Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Multi-agent systems
multi-robot coordination
0.912025
FlyKites: Human-Centric Interactive Exploration and Assistance Under Limited Communication · ICRA 2025
Machine learning › Reinforcement learning › exploration
multi-robot exploration
0.912025
FlyKites: Human-Centric Interactive Exploration and Assistance Under Limited Communication · ICRA 2025

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

relay assignment · 1.7human-in-the-loop simulation · 1.7distributed optimization · 1.7
YearPublicationVenuePosition
2025 FlyKites: Human-Centric Interactive Exploration and Assistance Under Limited Communication
abstract
Fleets of autonomous robots have been deployed for exploration of unknown scenes for features of interest, e.g., subterranean exploration, reconnaissance, search and rescue missions. During exploration, the robots may encounter un-identified targets, blocked passages, interactive objects, temporary failure, or other unexpected events, all of which require consistent human assistance with reliable communication for a time period. This however can be particularly challenging if the communication among the robots is severely restricted to only close-range exchange via ad-hoc networks, especially in extreme environments like caves and underground tunnels. This paper presents a novel human-centric interactive exploration and assistance framework called FlyKites, for multi-robot systems under limited communication. It consists of three interleaved components: (I) the distributed exploration and intermittent communication (called the “spread mode”), where the robots collaboratively explore the environment and exchange local data among the fleet and with the operator; (II) the simultaneous optimization of the relay topology, the operator path, and the assignment of robots to relay roles (called the”relay mode”), such that all requested assistance can be provided with minimum delay; (III) the human-in-the-loop online execution, where the robots switch between different roles and interact with the operator adaptively. Extensive human-in-the-loop simulations and hardware experiments are performed over numerous challenging scenes.
Zhuoli Tian, Jinsheng Wei
ICRA2