Yasemin Bekiroglu
Yasemin Bekiroglu is an Associate Professor (Docent) in the Automatic Control research group. She completed her Ph.D. at the Royal Institute of Technology (KTH) in 11/2012. Her research is focused on data-efficient learning from multisensory data for robotics applications. She received the Best Paper Award at IEEE International Conference on Robotics and Automation for Humanitarian Applications (RAHA) in 2016 and the Best Manipulation Paper Award at IEEE International Conference on Robotics and Automation (ICRA) in 2013, and was IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) CoTeSys Cognitive Robotics Best Paper Award Finalist in 2013. She serves as a reviewer and Associate Editor for robotics conferences and journals.
Showing 49 publications
A Unifying Variational Framework for Gaussian Process Motion Planning
Learning Dynamic Tasks on a Large-scale Soft Robot in a Handful of Trials
GraspAda: Deep Grasp Adaptation through Domain Transfer
Benchmarking Local Motion Planners for Navigation of Mobile Manipulators
Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces
Sliding Touch-Based Exploration for Modeling Unknown Object Shape with Multi-Fingered Hands
Neural Field Movement Primitives for Joint Modelling of Scenes and Motions
Safe Trajectory Sampling in Model-Based Reinforcement Learning
Affordance Transfer based on Self-Aligning Implicit Representations of Local Surfaces
Bayesian Optimization-based Nonlinear Adaptive PID Controller Design for Robust Mobile Manipulation
Simultaneous Tactile Exploration and Grasp Refinement
Enhanced GPIS Learning Based on Local and Global Focus Areas
Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects
Simultaneous Tactile Exploration and Grasp Refinement for Unknown Objects
Benchmarking Protocol for Grasp Planning Algorithms
Visual and Tactile 3D Point Cloud Data from Real Robots for Shape Modeling and Completion
Dynamic grasp and trajectory planning for moving objects
Evaluating the Quality of Non-Prehensile Balancing Grasps
Shape Modeling based on Sparse Gaussian Process Implicit Surfaces
Towards advanced robotic manipulation for nuclear decommissioning
A Database for Reproducible Manipulation Research: CapriDB - Capture, Print, Innovate
Hierarchical Fingertip Space: A Unified Framework for Grasp Planning and In-Hand Grasp Adaptation
Probabilistic Consolidation of Grasp Experience
Analytic Grasp Success Prediction with Tactile Feedback
Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces
Learning Predictive State Representation for In-Hand Manipulation
Grasp Moduli Spaces and Spherical Harmonics
Hierarchical Fingertip Space for Synthesizing Adaptable Fingertip Grasps
Learning to Disambiguate Object Hypotheses through Self-Exploration
Grasp Moduli Spaces, Gaussian Processes and Multimodal Sensor Data
Learning of Grasp Adaptation through Experience and Tactile Sensing
What's in the Container? Classifying Object Contents from Vision and Touch
Enhancing Visual Perception of Shape through Tactile Glances
Predicting Slippage and Learning Manipulation Affordances through Gaussian Process Regression
A Probabilistic Framework for Task-Oriented Grasp Stability Assessment
Grasp Stability from Vision and Touch
Learning Task- and Touch-based Grasping
Assessing grasp stability based on learning and haptic data
Learning Tactile Characterizations Of Object- And Pose-specific Grasps
Integrating Grasp Planning with Online Stability Assessment using Tactile Sensing
Joint Observation of Object Pose and Tactile Imprints for Online Grasp Stability Assessment
Learning grasp stability with tactile data and HMMs
Learning grasp stability based on haptic data
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Showing 1 research projects
Dexterous robot assistant for everyday physical object manipulation