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Zhixing Xue

dblp:03/3673 · DBLP profile ↗
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5ranked-venue papers
2as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 5 · 2 first-authorSystems, architecture and hardware · 5 · 2 first-author

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
4 papers
Robot manipulation · 91% Motion planning and robot control · 9%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Robotics › Robot manipulation
grasping
0.222011
An autonomous ice-cream serving robot · ICRA 2011
Learning of probabilistic grasping strategies using Programming by Demonstration · ICRA 2010
Robotics › Robot manipulation › autonomous manipulation
manipulation monitoring
0.112012
Monitoring of manipulation activities for a service robot using supervised learning · ICRA 2012
Robotics › Robot manipulation
deformable object manipulation
0.112011
An autonomous ice-cream serving robot · ICRA 2011
Robotics › Robot manipulation › grasping
grasp planning
0.112011
Graspability: A description of work surfaces for planning of robot manipulation sequences · ICRA 2011
Robotics › Robot manipulation
learning from demonstration
0.112010
Learning of probabilistic grasping strategies using Programming by Demonstration · ICRA 2010
Robotics › Motion planning and robot control › robot control
impedance control
0.012011
An autonomous ice-cream serving robot · ICRA 2011
Robotics › Motion planning and robot control › motion planning
manipulation planning
0.012011
Graspability: A description of work surfaces for planning of robot manipulation sequences · ICRA 2011

Methods — techniques the papers use, named apart from their topics

support vector machine · 0.1supervised learning · 0.1time-of-flight camera · 0.1reachability analysis · 0.1impedance control · 0.1discrete environment map · 0.1variation model · 0.1probabilistic motion planning · 0.1
YearPublicationVenuePosition
2012 Monitoring of manipulation activities for a service robot using supervised learning
abstract
To be a good helper, grasping and manipulation are the most important abilities of a service robot. It should be able to adapt its manipulation actions to new tasks and environments. During the execution, it is important to rate the success of actions, so that the robot can plan and execute further actions to correct and recover from the failed actions. The successful execution of manipulation actions depends on various factors during the whole execution, such as the position of the robotic hand and forces exerted by the robot. The goal of the manipulation action monitoring is to estimate the success state from the huge amount of data collected during the execution. The main challenge to solve this problem is to identify the success or failure state from the the high dimensional data collection. We propose a method to classify ongoing activities using a set of support vector machines (SVM). After a supervised training process with manually labeled successful or failure results, our system can correctly estimate the resulting state of a manipulation activity. We present experiments on our bimanual manipulation demonstrator and evaluate the results.
Steffen W. Ruehl, Zhixing Xue, Rüdiger Dillmann
ICRA2
2011 Graspability: A description of work surfaces for planning of robot manipulation sequences
abstract
For complex manipulation with multiple objects a service robot needs information about the structure of its environment including how and where it can manipulate in it. For this purpose, we introduce the Graspability. It is a measure describing the quality of a pose in Cartesian space for grasping or placing an object. The graspability considers kinematic reachability for a grasping robot and available grasps for the object. It is based on the assumption, that in manipulation tasks, objects tend to be located on a planar surfaces and have to be graspable from that plane, based on that assumption, we develop a discrete map of the environment which enables the use of the graspability in an autonomous planning system for complex manipulation tasks with multiple objects. Generated manipulation actions are evaluated on a real robot.
Steffen W. Ruehl, Andreas Hermann 0001, Zhixing Xue, Thilo Kerscher, Rüdiger Dillmann
ICRA3
2011 An autonomous ice-cream serving robot
abstract
An autonomous ice cream serving robot is presented in this video. The video was filmed during the Automatica 2010 trade fair in Munich. Within four days, approximately 250 scoops of different kinds of ice cream have been served to visitors during the fair. Using the KUKA light-weighted robotic arms and the DLR/HIT robotic hands, two scientific aspects are shown in this video: stable grasping of fragile objects and manipulation of cream-like mass using a time-of-flight camera. Impedance control are intensively used both by grasping and manipulation. The video shows an autonomous service robot scenario beyond simple fetch-and-carry chores.
Zhixing Xue, Steffen W. Ruehl, Andreas Hermann 0001, Thilo Kerscher, Rüdiger Dillmann
ICRA1
2010 Learning of probabilistic grasping strategies using Programming by Demonstration
abstract
The planning of grasping motions is demanding due to the complexity of modern robot systems. In Programming by Demonstration, the observation of a human teacher allows to draw additional information about grasping strategies. Rosell showed, that the motion planning problem can be simplified by globally restricting the set of valid configurations to a learned subspace. In this work, the transformation of a humanoid grasping strategy to an anthropomorphic robot system is described by a probabilistic model, called variation model, in order to account for modeling and transformation errors. The variation model resembles a soft preference for grasping motions similar to the demonstration and therefore induces a non-uniform sampling distribution on the configuration space. The sampling distribution is used in a standard probabilistic motion planner to plan grasping motions efficiently for new objects in new environments.
Rainer Jäkel, Sven R. Schmidt-Rohr, Zhixing Xue, Martin Lösch, Rüdiger Dillmann
ICRA3
2008 Dexterous manipulation planning of objects with surface of revolution
abstract
In this paper, we propose a novel method for dexterous manipulation planning problem of rotating object with surface of revolution using a robotic multi-fingered hand. This method finds contact point trajectories from contact points between the robotic hand and the object with task-orientated manipulation quality measurement. Based on the defined manipulation quality, the pose for robotic hand relative to object can also be optimized by random sample. Experiments using Schunk anthropomorphic hand with 13 degrees of freedom screwing a light bulb into holder with screw thread demonstrates the feasibility and efficiency of the introduced method.
Zhixing Xue, Johann Marius Zöllner, Rüdiger Dillmann
IROS1