Demo Data from EDBT 2026 / reviewers in the wild / expert
Ryan McGann
dblp:359/4710
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
—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 |
Motion planning and robot control · 50% Robot manipulation · 50% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Computational science and engineering · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
agricultural manipulation |
0.8 | 1 | 2024 | Containerized Vertical Farming Using Cobots · ICRA 2024 |
Robotics › Motion planning and robot control › motion planning › manipulation planning
manipulation task planning |
0.8 | 1 | 2024 | Containerized Vertical Farming Using Cobots · ICRA 2024 |
Computational science and engineering
agricultural robotics |
0.2 | 1 | 2024 | Containerized Vertical Farming Using Cobots · ICRA 2024 |
Methods — techniques the papers use, named apart from their topics
learning from demonstration · 1.5RGB-D camera · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Containerized Vertical Farming Using CobotsabstractContainerized vertical farming is a type of vertical farming practice using hydroponics in which plants are grown in vertical layers within a mobile shipping container. Space limitations within shipping containers make the automation of different farming operations challenging. In this paper, we explore the use of cobots (i.e., collaborative robots) to automate two key farming operations, namely, the transplantation of saplings and the harvesting of grown plants. Our method uses a single demonstration from a farmer to extract the motion constraints associated with the tasks, namely, transplanting and harvesting, and can then generalize to different instances of the same task. For transplantation, the motion constraint arises during insertion of the sapling within the growing tube, whereas for harvesting, it arises during extraction from the growing tube. We present experimental results to show that using RGBD camera images (obtained from an eye-in-hand configuration) and one demonstration for each task, it is feasible to perform transplantation of saplings and harvesting of leafy greens using a cobot, without task-specific programming. Dasharadhan Mahalingam, Aditya Patankar, Khiem Phi, Ryan McGann, I. V. Ramakrishnan |
ICRA | 5 |