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Xidi Xue

dblp:215/7709 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Robotics › Motion planning and robot control
motion planning
0.812024
Safe Table Tennis Swing Stroke with Low-Cost Hardware · ICRA 2024
Robotics › Motion planning and robot control
robot control
0.812024
Safe Table Tennis Swing Stroke with Low-Cost Hardware · ICRA 2024
Robotics › Motion planning and robot control › motion planning
safe motion planning
0.812024
Safe Table Tennis Swing Stroke with Low-Cost Hardware · ICRA 2024
Robotics › Motion planning and robot control › trajectory planning
trajectory time scaling
0.812024
Safe Table Tennis Swing Stroke with Low-Cost Hardware · ICRA 2024
Robotics › Robot manipulation › nonprehensile manipulation › dynamic manipulation
table tennis robot
0.212024
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
YearPublicationVenuePosition
2024 Safe Table Tennis Swing Stroke with Low-Cost Hardware
abstract
Playing 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
ICRA4