EDBT 2026 Demo / reviewers in the wild / expert
Xidi Xue
dblp:215/7709
· 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 · 93% Robot manipulation · 7% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Motion planning and robot control
motion planning |
0.8 | 1 | 2024 | Safe Table Tennis Swing Stroke with Low-Cost Hardware · ICRA 2024 |
Robotics › Motion planning and robot control
robot control |
0.8 | 1 | 2024 | Safe Table Tennis Swing Stroke with Low-Cost Hardware · ICRA 2024 |
Robotics › Motion planning and robot control › motion planning
safe motion planning |
0.8 | 1 | 2024 | Safe Table Tennis Swing Stroke with Low-Cost Hardware · ICRA 2024 |
Robotics › Motion planning and robot control › trajectory planning
trajectory time scaling |
0.8 | 1 | 2024 | Safe Table Tennis Swing Stroke with Low-Cost Hardware · ICRA 2024 |
Robotics › Robot manipulation › nonprehensile manipulation › dynamic manipulation
table tennis robot |
0.2 | 1 | 2024 | Safe Table Tennis Swing Stroke with Low-Cost Hardware · ICRA 2024 |
Methods — techniques the papers use, named apart from their topics
safety constraints · 0.8joint trajectory generation · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Safe Table Tennis Swing Stroke with Low-Cost HardwareabstractPlaying table tennis with a human player is a challenging robotic task due to its dynamic nature. Despite a number of researches being devoted to developing robotic table tennis systems, most of the works have demanding hardware requirements and ignore safety measures when generating the swing stoke. To address these issues, we propose a safe motion planning framework that fully pushes the robotic hardware performance limits to play table tennis. In particular, we propose a pipeline to generate manipulator joint trajectories with environmental safety constraints and scale the trajectories to satisfy joint movement limitations. We use three different agents to validate the planning algorithm with our handmade robot platform in both simulation and real-world environments. Francesco Cursi, Marcus Kalander, Shuang Wu 0005, Xidi Xue, Guangjian Tian, Xingyue Quan, Jianye Hao |
ICRA | 4 |