D. Kragic

Publications436
h-index60
Citations13,299
Highly Influential Citations594
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The GRASP Taxonomy of Human Grasp Types
TLDR
The resulting taxonomy incorporates all grasps found in the reviewed taxonomies that complied with the grasp definition and is shown that due to the nature of the classification, the 33 grasp types might be reduced to a set of 17 more generalgrasps if only the hand configuration is considered without the object shape/size.
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Data-Driven Grasp Synthesis—A Survey
TLDR
A review of the work on data-driven grasp synthesis and the methodologies for sampling and ranking candidate grasps and an overview of the different methodologies are provided, which draw a parallel to the classical approaches that rely on analytic formulations.
View on IEEE
Deep Representation Learning for Human Motion Prediction and Classification
TLDR
The results show that deep feedforward networks, trained from a generic mocap database, can successfully be used for feature extraction from human motion data and that this representation can be used as a foundation for classification and prediction.
View on IEEE
Adaptive Virtual Fixtures for Machine-Assisted Teleoperation Tasks
TLDR
The use of adaptive virtual fixtures that enable the ability to avoid unforeseen obstacles and deviate from the model are proposed and an on-line decision of how to fixture the movement is provided.
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ST-HMP: Unsupervised Spatio-Temporal feature learning for tactile data
TLDR
A new descriptor named Spatio-Temporal Hierarchical Matching Pursuit (ST-HMP) is proposed that captures properties of a time series of tactile sensor measurements that extracts rich spatio-temporal structures from raw tactile data without the need to predefine discriminative data characteristics.
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Dual arm manipulation - A survey
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Design of a flexible tactile sensor for classification of rigid and deformable objects
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Assessing Grasp Stability Based on Learning and Haptic Data
TLDR
A probabilistic learning framework to assess grasp stability is proposed and it is shown that knowledge about grasp stability can be inferred using information from tactile sensors, which opens a number of interesting venues for the future research.
View on IEEE
Mind the gap - robotic grasping under incomplete observation
TLDR
The proposed approach to object shape prediction aims at closing the knowledge gaps in the robot's understanding of the world by providing a completed state estimate of the environment to a simulator in which stable grasps and collision-free movements are planned.
View on IEEE
Action recognition and understanding through motor primitives
TLDR
This work deals with single arm/hand actions which are very similar to each other in terms of arm/ hand motions, and uses a combination of discriminative support vector machines and generative hidden Markov models to model the process.
View on Taylor & Francis
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