EDBT 2026 Demo / reviewers in the wild / expert
Jonathan Daudelin
dblp:198/7089
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2018
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Systems, architecture and hardware · 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 |
Legged, aerial and field robots · 44% Planning, search and constraint satisfaction · 44% Multi-agent systems · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Legged, aerial and field robots › mobile robot locomotion
modular robot locomotion |
0.3 | 1 | 2018 | Perception-Informed Autonomous Environment Augmentation with Modular Robots · ICRA 2018 |
Knowledge, reasoning and agents › Planning, search and constraint satisfaction
task planning |
0.3 | 1 | 2018 | Perception-Informed Autonomous Environment Augmentation with Modular Robots · ICRA 2018 |
Knowledge, reasoning and agents › Multi-agent systems
modular robotics |
0.1 | 1 | 2018 | Perception-Informed Autonomous Environment Augmentation with Modular Robots · ICRA 2018 |
Methods — techniques the papers use, named apart from their topics
perception algorithm · 0.3high-level planning · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2018 | Perception-Informed Autonomous Environment Augmentation with Modular RobotsabstractWe present a system enabling a modular robot to autonomously build structures in order to accomplish high-level tasks. Building structures allows the robot to surmount large obstacles, expanding the set of tasks it can perform. This addresses a common weakness of modular robot systems, which often struggle to traverse large obstacles. This paper presents the hardware, perception, and planning tools that comprise our system. An environment characterization algorithm identifies features in the environment that can be augmented to create a path between two disconnected regions of the environment. Specially-designed building blocks enable the robot to create structures that can augment the environment to make obstacles traversable. A high-level planner reasons about the task, robot locomotion capabilities, and environment to decide if and where to augment the environment in order to perform the desired task. We validate our system in hardware experiments. Tarik Tosun, Jonathan Daudelin, Gangyuan Jing, Hadas Kress-Gazit, Mark E. Campbell, Mark Yim |
ICRA | 2 |