Demo Data from SIGIR 2026 / reviewers in the wild / expert
Zhixing Xue
dblp:03/3673
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot manipulation
grasping |
0.2 | 2 | 2011 | 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 |
0.1 | 1 | 2012 | Monitoring of manipulation activities for a service robot using supervised learning · ICRA 2012 |
Robotics › Robot manipulation
deformable object manipulation |
0.1 | 1 | 2011 | An autonomous ice-cream serving robot · ICRA 2011 |
Robotics › Robot manipulation › grasping
grasp planning |
0.1 | 1 | 2011 | Graspability: A description of work surfaces for planning of robot manipulation sequences · ICRA 2011 |
Robotics › Robot manipulation
learning from demonstration |
0.1 | 1 | 2010 | Learning of probabilistic grasping strategies using Programming by Demonstration · ICRA 2010 |
Robotics › Motion planning and robot control › robot control
impedance control |
0.0 | 1 | 2011 | An autonomous ice-cream serving robot · ICRA 2011 |
Robotics › Motion planning and robot control › motion planning
manipulation planning |
0.0 | 1 | 2011 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2012 | Monitoring of manipulation activities for a service robot using supervised learningabstractTo 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 |
ICRA | 2 |
| 2011 | Graspability: A description of work surfaces for planning of robot manipulation sequencesabstractFor 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 |
ICRA | 3 |
| 2011 | An autonomous ice-cream serving robotabstractAn 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 |
ICRA | 1 |
| 2010 | Learning of probabilistic grasping strategies using Programming by DemonstrationabstractThe 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 |
ICRA | 3 |
| 2008 | Dexterous manipulation planning of objects with surface of revolutionabstractIn 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 |
IROS | 1 |