Demo Data from SIGIR 2026 / reviewers in the wild / expert
Giulio Troso
dblp:41/3660
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none
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 |
Reinforcement learning · 87% Robot navigation and mapping · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Reinforcement learning
exploration |
0.0 | 1 | 2004 | The SRT Method: Randomized Strategies for Exploration · ICRA 2004 |
Machine learning › Reinforcement learning › exploration › autonomous exploration
sensor-based exploration |
0.0 | 1 | 2004 | The SRT Method: Randomized Strategies for Exploration · ICRA 2004 |
Robotics › Robot navigation and mapping
map building |
0.0 | 1 | 2004 | The SRT Method: Randomized Strategies for Exploration · ICRA 2004 |
Methods — techniques the papers use, named apart from their topics
randomized incremental tree · 0.0perception strategies · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2004 | The SRT Method: Randomized Strategies for ExplorationabstractWe present a method for sensor-based exploration of unknown environments by a mobile robot. The method is based on the randomized incremental generation of a data structure called sensor-based random tree (SRT), which represents a roadmap of the explored area with an associated safe region. Different exploration strategies may be obtained by instantiating the general method with different perception techniques. Two such techniques are discussed: the first, conservative and particularly suited to noisy sensors, results in an exploration strategy called SRT-Ball. The second perception technique is more confident, and the corresponding strategy is called SRT-Star. The two strategies are critically compared by simulations as well as by experiments on the MagellanPro robot. Giuseppe Oriolo, Marilena Vendittelli, Luigi Freda, Giulio Troso |
ICRA | 4 |