Mayank Patel 0001

dblp:232/9722-1 · DBLP profile ↗
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3ranked-venue papers
1as first author
2since 2021 · last 2026
0000-0002-7804-4017ORCID · verified

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 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous computing · 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.

Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%
Artificial intelligence
1 paper
Robot manipulation · 50% Robot navigation and mapping · 50%

Topics — the 1 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Human-robot interaction
service robot
0.612022
Refilling Water Bottles in Elderly Care Homes With the Help of a Safe Service Robot · HRI 2022

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

technical evaluation · 1.1questionnaire · 1.1pose detection · 1.1
YearPublicationVenuePosition
2026 RAW-HF: Resource Availability & Workload-aware Hybrid Framework for raw data query processing
Mayank Patel 0001, Minal Bhise
Future Gener. Comput. Syst.1
2022 Refilling Water Bottles in Elderly Care Homes With the Help of a Safe Service Robot
abstract
This study presents key technologies of a mobile service robot developed to manipulate objects around people safely. We demonstrate this ability to support staff in elderly care homes in the future. The take-over by a service robot allows the staff to spend less time with routine logistical tasks and therefore better focus on the interaction with residents. In the selected application scenario, the robot helps staff by (1) retrieving empty bottles from the residents' rooms, (2) bringing them to the kitchen, (3) taking the refilled bottles back to a table inside the residents' rooms. This task seems trivial for a person, but the robot needs to orchestrate numerous algorithms and components to work smoothly, such as bottle pose detection, manipulation, and navigation. A technical evaluation indicates a high performance of single components, but due to isolated failures, the overall scenario does not always succeed. Next to the technical aspects, it is fundamental to determine the acceptance of the robot, which was achieved by analyzing questionnaires given to care workers. Finally, this paper presents lessons learned to help other researchers in similar use-cases.
Çagatay Odabasi, Florenz Graf, Jochen Lindermayr, Mayank Patel 0001, Simon D. Baumgarten, Birgit Graf
HRI4
2019 Towards Automated Order Picking Robots for Warehouses and Retail
Richard Bormann, Bruno Brito, Jochen Lindermayr, Marco Omainska, Mayank Patel 0001
ICVS5