Mårten Björkman

发表

Real-Time Motion and Stereo Cues for Active Visual Observers

Mårten Björkman, 2002 .

Skew-Explore: Learn faster in continuous spaces with sparse rewards

Patric Jensfelt, Xi Chen, Mårten Björkman, 2019 .

Human-Centered Collaborative Robots With Deep Reinforcement Learning

Danica Kragic, Xi Chen, Mårten Björkman, 2020, IEEE Robotics and Automation Letters.

Strategies for Object Manipulation using Foveal and Peripheral Vision

Danica Kragic, Mårten Björkman, 2006, Fourth IEEE International Conference on Computer Vision Systems (ICVS'06).

Real-Time Epipolar Geometry Estimation of Binocular Stereo Heads

Jan-Olof Eklundh, Mårten Björkman, Mårten Björkman, 2002, IEEE Trans. Pattern Anal. Mach. Intell..

Integration of Visual and Shape Attributes for Object Action Complexes

Danica Kragic, Mårten Björkman, Kai Huebner, 2008, ICVS.

Learning to disambiguate object hypotheses through self-exploration

Mårten Björkman, Yasemin Bekiroglu, 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

Combination of foveal and peripheral vision for object recognition and pose estimation

Danica Kragic, Mårten Björkman, 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

Generating object hypotheses in natural scenes through human-robot interaction

Niklas Bergström, Danica Kragic, Mårten Björkman, 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Classifying Multivariate Electrocorticographic Signal Patterns from dierent sessions

Mårten Björkman, Dennis Sångberg, Oskar Segersvärd, 2013 .

Acting and Interacting in the Real World

Danica Kragic, Jeannette Bohg, Mårten Björkman, 2011 .

Vision for robotic object manipulation in domestic settings

Danica Kragic, Henrik I. Christensen, Jan-Olof Eklundh, 2005, Robotics Auton. Syst..

Object appearance from integration of 3D and 2D cues in real scenes

Jan-Olof Eklundh, Mårten Björkman, Eric Hayman, 2010 .

Attending, Foveating and Recognizing Objects in Real World Scenes

Jan-Olof Eklundh, Mårten Björkman, 2004, BMVC.

Real-time epipolar geometry estimation and disparity

Mårten Björkman, Jan-Olof Eklundh, Mårten Björkman, 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

Exploring Temporal Dependencies in Multimodal Referring Expressions with Mixed Reality

Danica Kragic, Elena Sibirtseva, Mårten Björkman, 2019, HCI.

On the Geometry of Rectifier Convolutional Neural Networks

Mårten Björkman, Hossein Azizpour, Stefan Carlsson, 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).

An Active Vision System for Detecting, Fixating and Manipulating Objects in the Real World

Danica Kragic, Mårten Björkman, Kai Huebner, 2010, Int. J. Robotics Res..

A framework for vision based bearing only 3D SLAM

Danica Kragic, John Folkesson, Patric Jensfelt, 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

Generating object hypotheses in natural scenes through human-robot interaction

Danica Kragic, Mårten Björkman, Niklas Bergstrom, 2011, IROS 2011.

Vision in the real world: Finding, attending and recognizing objects

Jan-Olof Eklundh, Mårten Björkman, Mårten Björkman, 2006, Int. J. Imaging Syst. Technol..

Scene Representation and Object Grasping Using Active Vision

Danica Kragic, Jeannette Bohg, Mårten Björkman, 2010 .

Recognition of Objects in the Real World from a Systems Perspective

Jan-Olof Eklundh, Mårten Björkman, 2005, Künstliche Intell..

Visual and tactile 3D point cloud data from real robots for shape modeling and completion

Danica Kragic, Mårten Björkman, Carl Henrik Ek, 2020, Data in brief.

Foveated Figure-Ground Segmentation and Its Role in Recognition

Jan-Olof Eklundh, Mårten Björkman, Mårten Björkman, 2005, BMVC.

Grasp Moduli Spaces, Gaussian Processes and Multimodal Sensor Data

Danica Kragic, Mårten Björkman, Florian T. Pokorny, 2014 .

Detecting, segmenting and tracking unknown objects using multi-label MRF inference

Niklas Bergström, Danica Kragic, Mårten Björkman, 2014, Comput. Vis. Image Underst..

Object shape estimation and modeling, based on sparse Gaussian process implicit surfaces, combining visual data and tactile exploration

Mårten Björkman, Carl Henrik Ek, Rustam Stolkin, 2020, Robotics Auton. Syst..

YES - YEt another object segmentation: Exploiting camera movement

Danica Kragic, Mårten Björkman, Lazaros Nalpantidis, 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Attending, Foveating and Recognizing Objects in Real World Scenes

Jan-Olof Eklundh, Mårten Björkman, 2004 .

Adversarial Feature Training for Generalizable Robotic Visuomotor Control

Patric Jensfelt, Mårten Björkman, Ali Ghadirzadeh, 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

Issues and Strategies for Robotic Object Manipulation in Domestic Settings

Danica Kragic, Henrik I. Christensen, Jan-Olof Eklundh, 2004 .

Scene Understanding through Autonomous Interactive Perception

Niklas Bergström, Danica Kragic, Mårten Björkman, 2011, ICVS.

Active 3D Segmentation through Fixation of Previously Unseen Objects

Danica Kragic, Mårten Björkman, D. Kragic, 2010, BMVC.

Feature Descriptors for Tracking by Detection: a Benchmark pdf

Niklas Bergström, Danica Kragic, Mårten Björkman, 2016, ArXiv.

Real-time texture detection using the LU-transform

Jan-Olof Eklundh, Alireza Tavakoli Targhi, Mårten Björkman, 2006 .

Imitating by Generating: Deep Generative Models for Imitation of Interactive Tasks

Danica Kragic, Özge Öztimur Karadag, Mårten Björkman, 2019, Frontiers in Robotics and AI.

Enhancing visual perception of shape through tactile glances

Danica Kragic, Mårten Björkman, Yasemin Bekiroglu, 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Attention-based active 3D point cloud segmentation

Danica Kragic, Jeannette Bohg, Matthew Johnson-Roberson, 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Reducing the Read-Miss Penalty for Flat COMA Protocols

Per Stenström, Mårten Björkman, Fredrik Dahlgren, 1997, Comput. J..

Amortized Variational Inference for Road Friction Estimation

Mårten Björkman, L. Srikar Muppirisetty, Yiannis Karayiannidis, 2020, 2020 IEEE Intelligent Vehicles Symposium (IV).

Meta-Learning for Multi-objective Reinforcement Learning

Xi Chen, Patric Jensfelt, Mårten Björkman, 2018, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Group Behavior Recognition Using Attention- and Graph-Based Neural Networks

Fangkai Yang, Christopher E. Peters, Mårten Björkman, 2020, ECAI.

Intrinsic camera and hand-eye calibration for a robot vision system using a point marker

Petter Ögren, Mårten Björkman, Ivan Lundberg, 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

A Sensorimotor Learning Framework for Object Categorization

Danica Kragic, Atsuto Maki, Mårten Björkman, 2016, IEEE Transactions on Cognitive and Developmental Systems.

Active 3D scene segmentation and detection of unknown objects

Danica Kragic, Mårten Björkman, D. Kragic, 2010, 2010 IEEE International Conference on Robotics and Automation.

Strategies for multi-modal scene exploration

Danica Kragic, Jeannette Bohg, Matthew Johnson-Roberson, 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Interactive object classification using sensorimotor contingencies

Danica Kragic, Mårten Björkman, Virgile Hogman, 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

Self-learning and adaptation in a sensorimotor framework

Danica Kragic, Mårten Björkman, Ali Ghadirzadeh, 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

Visual Cues for a Fixating Active Agent

Jan-Olof Eklundh, Mårten Björkman, 2001, RobVis.

Using hints to reduce the read miss penalty for flat COMA protocols

Per Stenström, Mårten Björkman, Fredrik Dahlgren, 1995, Proceedings of the Twenty-Eighth Annual Hawaii International Conference on System Sciences.

A real-time system for epipolar geometry and ego-motion estimation

Jan-Olof Eklundh, Mårten Björkman, Mårten Björkman, 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

Learning visual forward models to compensate for self-induced image motion

Gert Kootstra, Atsuto Maki, Mårten Björkman, 2014, The 23rd IEEE International Symposium on Robot and Human Interactive Communication.

Impact of Trajectory Generation Methods on Viewer Perception of Robot Approaching Group Behaviors

Fangkai Yang, Mårten Björkman, Christopher Peters, 2020, 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN).

Data-efficient visuomotor policy training using reinforcement learning and generative models pdf

Danica Kragic, Mårten Björkman, Ville Kyrki, 2020, ArXiv.

A sensorimotor approach for self-learning of hand-eye coordination

Atsuto Maki, Mårten Björkman, Ali Ghadirzadeh, 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Deep predictive policy training using reinforcement learning

Danica Kragic, Atsuto Maki, Mårten Björkman, 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Hyperplane Arrangements of Trained ConvNets Are Biased pdf

Mårten Björkman, Hossein Azizpour, Stefan Carlsson, 2020, ArXiv.

A sensorimotor reinforcement learning framework for physical Human-Robot Interaction

Danica Kragic, Atsuto Maki, Mårten Björkman, 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Classification of Electroencephalographic Signals For Brain-Computer Interface

Mårten Björkman, Richard Nordström, Annika Tängmark, 2013 .

A World Model Based Reinforcement Learning Architecture for Autonomous Power System Control

Lars Nordström, Mårten Björkman, Gunnar Ingeström, 2021, 2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).

Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms

Danica Kragic, Chelsea Finn, Mårten Björkman, 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Combining Planning and Learning of Behavior Trees for Robotic Assembly pdf

Matteo Iovino, Jonathan Styrud, Mårten Björkman, 2021, 2022 International Conference on Robotics and Automation (ICRA).

Monte Carlo Filtering Objectives

Yiannis Karayiannidis, Mårten Björkman, Sihao Ding, 2021, IJCAI.

Coordinating With a Robot Partner Affects Neural Processing Related to Action Monitoring

Danica Kragic, Peter König, Mårten Björkman, 2021, bioRxiv.

Graph-based Normalizing Flow for Human Motion Generation and Reconstruction

Danica Kragic, Hang Yin, Mårten Björkman, 2021, 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN).

Monte Carlo Filtering Objectives: A New Family of Variational Objectives to Learn Generative Model and Neural Adaptive Proposal for Time Series pdf

Yiannis Karayiannidis, Mårten Björkman, Sihao Ding, 2021, ArXiv.