2016 - IEEE Fellow For contributions to vision-based systems and robotic object manipulation
Her primary areas of investigation include Artificial intelligence, Robot, Computer vision, Robotics and GRASP. The concepts of her Artificial intelligence study are interwoven with issues in Machine learning and Task. The various areas that Danica Kragic examines in her Robot study include Grippers, Identification, Artificial neural network and Set.
Her study looks at the relationship between Computer vision and topics such as Mobile robot, which overlap with Control theory. Her Robotics research is multidisciplinary, incorporating perspectives in Teleoperation, Visual servoing, Human–computer interaction and Tactile sensor. Her research integrates issues of Stability, Representation, Programming by demonstration and Haptic technology in her study of GRASP.
Artificial intelligence, Robot, Computer vision, Object and Robotics are her primary areas of study. Her Artificial intelligence study integrates concerns from other disciplines, such as Machine learning, Task, Human–computer interaction and GRASP. The study incorporates disciplines such as Programming by demonstration, Tactile sensor, Stability, Representation and Grippers in addition to GRASP.
Danica Kragic combines subjects such as Motion, Probabilistic logic and Reinforcement learning with her study of Robot. The study of Computer vision is intertwined with the study of Robustness in a number of ways. Her work deals with themes such as Representation, Segmentation and Set, which intersect with Object.
Danica Kragic mainly investigates Artificial intelligence, Robot, Robotics, Task and Object. Danica Kragic does research in Artificial intelligence, focusing on Interpretability specifically. Her biological study spans a wide range of topics, including Motion, Computer vision, Probabilistic logic and Task analysis.
Her Computer vision research incorporates elements of Task oriented, Sequence and State space. Her Task research incorporates themes from Human–computer interaction, Adaptation and Reinforcement learning. Her Object research includes themes of GRASP, State, Hand manipulation, Task and Configuration space.
Danica Kragic mainly focuses on Artificial intelligence, Robotics, Object, Robot and Human–computer interaction. Danica Kragic has researched Artificial intelligence in several fields, including Machine learning and Task. As a member of one scientific family, Danica Kragic mostly works in the field of Object, focusing on Benchmarking and, on occasion, Table, Measure and State.
Her Robot study combines topics from a wide range of disciplines, such as Probabilistic logic, Deep learning and Task analysis. Her Task analysis research includes elements of Visualization and Computer vision. Her Human–computer interaction research is multidisciplinary, relying on both Video tracking and Motion planning.
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Data-Driven Grasp Synthesis—A Survey
Jeannette Bohg;Antonio Morales;Tamim Asfour;Danica Kragic.
IEEE Transactions on Robotics (2014)
The GRASP Taxonomy of Human Grasp Types
Thomas Feix;Javier Romero;Heinz-Bodo Schmiedmayer;Aaron M. Dollar.
IEEE Transactions on Human-Machine Systems (2016)
Short survey: Dual arm manipulation-A survey
Christian Smith;Yiannis Karayiannidis;Lazaros Nalpantidis;Xavi Gratal.
Robotics and Autonomous Systems (2012)
IEEE/RSJ International Conference on Intelligent Robots and Systems
Marianna Madry;Heydar Maboudi Afkham;Carl Henrik Ek;Stefan Carlsson.
Survey on Visual Servoing for Manipulation
Danica Kragic;Henrik I Christensen.
usenix annual technical conference (2002)
Visual object-action recognition: Inferring object affordances from human demonstration
Hedvig Kjellström;Javier Romero;Danica Kragić.
Computer Vision and Image Understanding (2011)
Minimum volume bounding box decomposition for shape approximation in robot grasping
K. Huebner;S. Ruthotto;D. Kragic.
international conference on robotics and automation (2008)
The meaning of action: a review on action recognition and mapping
Volker Krüger;Danica Kragic;Aleš Ude;Christopher Geib.
Advanced Robotics (2007)
Deep Representation Learning for Human Motion Prediction and Classification
Judith Butepage;Michael J. Black;Danica Kragic;Hedvig Kjellstrom.
computer vision and pattern recognition (2017)
Human-Machine Collaborative Systems for Microsurgical Applications
Danica Kragic;Panadda Marayong;Ming Li;Allison M. Okamura.
international symposium on robotics (2005)
Profile was last updated on December 6th, 2021.
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