Johannes Andreas Stork

Johannes Andreas Stork

Associate Senior Lecturer

AASS Research Center
School of Science and Technology
Örebro University
70182 Örebro, Sweden
Room T1215
Phone +46 (0)19 30 39 40
johannesandreas.stork@oru.se

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About

My name is Johannes A. Stork and I am an Associate Senior Lecturer and WASP Assistant Professor of machine learning at Örebro University where I work at the Center for Applied Autonomous Sensor Systems (AASS). There, I also joined the Autonomous Mobile Manipulation Lab (AMM) as a founding member. My main research interests are in machine learning with interpretable models and autonomous intelligent systems.

Short Bio. In spring of 2020, I started my position as WASP Assistant Professor as part of the Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden’s largest ever individual research program and major national initiative for strategic basic research, education, and faculty recruitment. From 2018 to 2020, I held a position as Associate Senior Lecturer as part of the vice chancellor’s strategic initiative for faculty development. Before joining AASS in 2018, I was a post-doctoral researcher and before that PhD. student in Computer Science (Computer Vision and Robotics) at the Computer Vision and Active Perception Lab (CVAP) at the Royal Institute of Technology (KTH) in Stockholm. I did my post-graduate research as a member of Danica Kragic’s research group where my co-supervisors were Carl Henrik Ek and Yasemin Bekiroglu. Before that, I spent several years of my undergraduate studies at the University of Freiburg, Germany, as a student research assistant at the Social Robotics Lab of Kai O. Arras. I hold a MSc. and a BSc. degree in Computer Science with concentration in Artificial Intelligence and Robotics from Freiburg University.

News and Recent Results

June 15, 2021 (Open position)

We have an opening for a doctoral student position in machine learning.

June 14, 2021 (Project granted)

Our KKS Synergy project TeamRob where I am involved in two of the sub-projects has been granted.

May 5, 2021 (Open position)

We have an opening for a post-doc in machine learning.

April 1, 2021 (Project start)

Our Vinnova SIP-STRIM project with company partners Epiroc and Algoryx starts.

February 15, 2020 (New team member)

Jean-Paul Ivan joins the team as a doctoral student.

September 23, 2020 (Publication accepted)

Science Robotics print my commentary paper about learning to adapt.

November 2, 2020 (Project completed)

Our Vinnova SIP-STRIM pre-study project HosePredict with company partner Epiroc on modeling hydraulic hoses for underground mining machines finished successfully.

April 1, 2020

I am starting my new position as WASP Assistant Professor of machine learning.

Research Projects

I am is currently involved the following research projects:

  • Safe Remote Drilling through Predictive Modeling of Hydraulic Hoses, Vinnova SIP-STRIM, key person / co-applicant
  • Predictive Modeling of Hydraulic Hoses for Underground Mining / HosePredict, Vinnova SIP-STRIM pre-study, principle investigator
  • WASP Assistant Processor, Wallenberg AI, Autonomous Systems and Software Program, principle investigator

Group and Supervision

Doctoral students

I am currently (co-)supervising the following doctoral students:

  • Yuxuan Yang (Örebro University), Deformable Linear Object Model Learning with Physics-based Priors, with T. Stoyanov
  • Quantao Yang (Örebro University), Reinforcement Learning in Continuous Spaces with Interactively Acquired Knowledge-based Models, with T. Stoyanov
  • Jean-Paul Ivan (Örebro University), Robust Gaussian Process for Continual Learning, with T. Stoyanov
  • David Caceres Dominguez (Örebro University, Suzuki), Mobile Manipulator Co-worker for Automation of the Drawing Line, with T. Stoyanov and E. Schaffernicht
  • Marco Iannotta (Örebro University, Suzuki), Mobile Manipulator Co-worker for Assembly, with T. Stoyanov and E. Schaffernicht
  • Alan Lahoud (Örebro University, H&M), Combining Machine Learning and Optimization for Decision Making, with F. Pecora and M. Trincavelli
  • Andrey Rudenko (Örebro University, Bosch), Human Motion Prediction for Service Robots, with A.J. Lilienthal and K.O. Arras
  • Tiago Almeida (Örebro University), Learning Human Behaviors from Human Robot Interaction, with O. Martinez Mozos and A.J. Lilienthal
  • Dino Hüllmann (Örebro University, BAM), Gas Source Localization and Gas Distribution Mapping using an Open Path Gas Detector and Unmanned Aircraft, with A.J. Lilienthal and P.P. Neumann

Alumni Doctoral Students

I previously (co-)supervised the following doctoral students:

  • Isac Arnekvist (KTH Royal Institute of Technology), Transfer Learning using low-dimensional Representations in Reinforcement Learning, with D. Kragic
  • Mia Kokic (KTH Royal Institute of Technology), Learning for Task-oriented Grasping, with D. Kragic
  • Joshua Haustein (KTH Royal Institute of Technology), Robot Manipulation Planning Among Obstacles: Grasping, Placing and Rearranging, with D. Kragic
  • Weihao Yuan (Hong Kong University of Science and Technology), Learning Perception and Motion Planning in Robotic Manipulation, with M.Y. Wang
  • Haoran Song (Hong Kong University of Science and Technology), TBA, with M.Y. Wang
  • Akshaya Thippur (KTH Royal Institute of Technology), TBA, with P. Jensfelt

Teaching

  • Learning Theory and Reinforcement Learning (2020), WASP Graduate School, PhD.-level course, course responsible
  • DT505A: Sensors and Sensing (2020, 2021), Örebro University, Master-level course, course responsible
  • DT506A: AI on the Web (2020, 2021), Örebro Univeristy, Master-level course, course responsible
  • Reinforcement Learning Parts 1 and 2 (2019, 2020, 2021), SMART(ER) at Örebro University, Master-level course, course responsible
  • DD3359: Reinforcement Learning (2017, 2018), KTH Royal Institute of Technology, PhD.-level course, lecturing and course management
  • DT2359: Artificial Intelligence (2012, 2013, 2014, 2015), KTH Royal Institute of Technology, Master-level course, lecturing and tutoring

Publications

Journal Articles

[1] F. Rietz, S. Magg, F. Heintz, T. Stoyanov, S. Wermter and J. A. Stork. Hierarchical goals contextualize local reward decomposition explanations. Neural Computing & Applications 2022 [ BibTeX | DiVA ]
[2] Y. Yang, J. A. Stork and T. Stoyanov. Learning differentiable dynamics models for shape control of deformable linear objects. Robotics and Autonomous Systems, 158, 2022 [ BibTeX | DiVA ]
[3] Q. Yang, J. A. Stork and T. Stoyanov. MPR-RL : Multi-Prior Regularized Reinforcement Learning for Knowledge Transfer. IEEE Robotics and Automation Letters, 7(3):7652-7659, 2022 [ BibTeX | DiVA ]
[4] J. P. A. Ivan, T. Stoyanov and J. A. Stork. Online Distance Field Priors for Gaussian Process Implicit Surfaces. IEEE Robotics and Automation Letters, 7(4):8996-9003, 2022 [ BibTeX | DiVA ]
[5] Q. Yang, A. Dürr, E. A. Topp, J. A. Stork and T. Stoyanov. Variable Impedance Skill Learning for Contact-Rich Manipulation. IEEE Robotics and Automation Letters, 7(3):8391-8398, 2022 [ BibTeX | DiVA | PDF ]
[6] P. Güler, J. A. Stork and T. Stoyanov. Visual state estimation in unseen environments through domain adaptation and metric learning. Frontiers in Robotics and AI, 9, 2022 [ BibTeX | DiVA ]
[7] D. C. Hoang, J. A. Stork and T. Stoyanov. Voting and Attention-Based Pose Relation Learning for Object Pose Estimation From 3D Point Clouds. IEEE Robotics and Automation Letters, 7(4):8980-8987, 2022 [ BibTeX | DiVA ]
[8] J. A. Stork. Preparing to adapt is key for Olympic curling robots. Science robotics, 5(46), 2020 [ BibTeX | DiVA ]
[9] W. Yuan, K. Hang, D. Kragic, M. Y. Wang and J. A. Stork. End-to-end nonprehensile rearrangement with deep reinforcement learning and simulation-to-reality transfer. Robotics and Autonomous Systems, 119:119-134, 2019 [ BibTeX | DiVA ]
[10] K. Hang, X. Lyu, H. Song, J. A. Stork, A. Dollar, D. Kragic and F. Zhang. Perching and resting : A paradigm for UAV maneuvering with modularized landing gears. Science Robotics, 4(28), 2019 [ BibTeX | DiVA ]
[11] K. Hang, J. A. Stork, N. S. Pollard and D. Kragic. A Framework For Optimal Grasp Contact Planning. IEEE Robotics and Automation Letters, 2(2):704-711, 2017 [ BibTeX | DiVA ]
[12] K. Hang, M. Li, J. A. Stork, Y. Bekiroglu, F. T. Pokorny, A. Billard and D. Kragic. Hierarchical fingertip space : A unified framework for grasp planning and in-hand grasp adaptation. IEEE Transactions on robotics, 32(4):960-972, 2016 [ BibTeX | DiVA ]

Refereed Conference and Workshop Articles

[1] D. C. Hoang, J. A. Stork and T. Stoyanov. Context-Aware Grasp Generation in Cluttered Scenes. In IEEE International Conference on Robotics and Automation (ICRA 2022), Philadelphia, USA, May 23-27, 2022 2022 [ BibTeX | DiVA | PDF ]
[2] Q. Yang, A. Dürr, E. A. Topp, J. A. Stork and T. Stoyanov. Learning Impedance Actions for Safe Reinforcement Learning in Contact-Rich Tasks. In NeurIPS 2021 Workshop on Deployable Decision Making in Embodied Systems (DDM) 2021 [ BibTeX | DiVA | PDF ]
[3] Y. Yang, J. A. Stork and T. Stoyanov. Learning to Propagate Interaction Effects for Modeling Deformable Linear Objects Dynamics. In 2021 IEEE International Conference on Robotics and Automation (ICRA) : IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021, pages 1950-1957, 2021 [ BibTeX | DiVA | PDF ]
[4] Q. Yang, J. A. Stork and T. Stoyanov. Null space based efficient reinforcement learning with hierarchical safety constraints. In 2021 European Conference on Mobile Robots (ECMR) 2021 [ BibTeX | DiVA | PDF ]
[5] J. A. Stork and T. Stoyanov. Ensemble of Sparse Gaussian Process Experts for Implicit Surface Mapping with Streaming Data. In IEEE International Conference on Robotics and Automation, pages 10758-10764, 2020 [ BibTeX | DiVA ]
[6] H. Song, J. A. Haustein, W. Yuan, K. Hang, M. Y. Wang, D. Kragic and J. A. Stork. Multi-Object Rearrangement with Monte Carlo Tree Search : A Case Study on Planar Nonprehensile Sorting. , pages 9433-9440, 2020 [ BibTeX | DiVA ]
[7] A. Isac, C. J. Frederico, D. Kragic and J. A. Stork. The effect of Target Normalization and Momentum on Dying ReLU. In The 32nd annual workshop of the Swedish Artificial Intelligence Society (SAIS) 2020 [ BibTeX | DiVA ]
[8] I. Mitsioni, Y. Karayiannidis, J. A. Stork and D. Kragic. Data-Driven Model Predictive Control for the Contact-Rich Task of Food Cutting. In IEEE-RAS International Conference on Humanoid Robots, pages 244-250, 2019 [ BibTeX | DiVA ]
[9] J. A. Haustein, K. Hang, J. A. Stork and D. Kragic. Object Placement Planning and optimization for Robot Manipulators. In IEEE International Conference on Intelligent Robots and Systems, pages 7417-7424, 2019 [ BibTeX | DiVA ]
[10] W. Yuan, K. Hang, H. Song, D. Kragic, M. Y. Wang and J. A. Stork. Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation. In 2019 International Conference on Robotics and Automation (ICRA), pages 2153-2160, 2019 [ BibTeX | DiVA ]
[11] I. Arnekvist, D. Kragic and J. A. Stork. VPE : Variational Policy Embedding for Transfer Reinforcement Learning. In 2019 International Conference on Robotics and Automation (ICRA), pages 36-42, 2019 [ BibTeX | DiVA ]
[12] R. Antonova, M. Kokic, J. A. Stork and D. Kragic. Global Search with Bernoulli Alternation Kernel for Task-oriented Grasping Informed by Simulation. In Proceedings of Machine Learning Research : Conference on Robot Learning 2018, 87:641-650, 2018 [ BibTeX | DiVA ]
[13] J. A. Haustein, I. Arnekvist, J. A. Stork, K. Hang and D. Kragic. Non-prehensile Rearrangement Planning with Learned Manipulation States and Actions. 2018 [ BibTeX | DiVA ]
[14] W. Yuan, J. A. Stork, D. Kragic, M. Y. Wang and K. Hang. Rearrangement with Nonprehensile Manipulation Using Deep Reinforcement Learning. In 2018 IEEE International Conference on Robotics and Automation (ICRA), pages 270-277, 2018 [ BibTeX | DiVA ]
[15] M. Kokic, J. A. Stork, J. A. Haustein and D. Kragic. Affordance detection for task-specific grasping using deep learning. In 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), pages 91-98, 2017 [ BibTeX | DiVA ]
[16] A. Thippur, J. A. Stork and P. Jensfelt. Non-Parametric Spatial Context Structure Learning for Autonomous Understanding of Human Environments. In 2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), pages 1317-1324, 2017 [ BibTeX | DiVA ]
[17] Y. Bekiroglu, A. Damianou, R. Detry, J. A. Stork, D. Kragic and C. H. Ek. Probabilistic consolidation of grasp experience. In 2016 IEEE International Conference on Robotics and Automation (ICRA), pages 193-200, 2016 [ BibTeX | DiVA ]
[18] J. A. Stork, C. H. Ek, Y. Bekiroglu and D. Kragic. Learning Predictive State Representation for In-Hand Manipulation. In 2015 IEEE International Conference on Robotics and Automation (ICRA), pages 3207-3214, 2015 [ BibTeX | DiVA ]
[19] J. A. Stork, C. H. Ek and D. Kragic. Learning Predictive State Representations for planning. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 3427-3434, 2015 [ BibTeX | DiVA ]
[20] J. A. Stork, C. H. Ek and D. Kragic. Learning Predictive State Representations for planning. 2015 [ BibTeX | DiVA ]
[21] K. Hang, J. A. Stork, F. T. Pokorny and D. Kragic. Combinatorial optimization for hierarchical contact-level grasping. In 2014 IEEE International Conference on Robotics and Automation (ICRA), pages 381-388, 2014 [ BibTeX | DiVA ]
[22] A. Marzinotto, J. A. Stork, D. V. Dimarogonas and D. Kragic. Cooperative grasping through topological object representation. In 2014 IEEE-RAS International Conference on Humanoid Robots, pages 685-692, 2014 [ BibTeX | DiVA ]
[23] K. Hang, J. A. Stork and D. Kragic. Hierarchical fingertip space for multi-fingered precision grasping. In 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1641-1648, 2014 [ BibTeX | DiVA ]
[24] K. Hang, M. Li, J. A. Stork, Y. Bekiroglu, A. Billard and D. Kragic. Hierarchical Fingertip Space for Synthesizing Adaptable Fingertip Grasps. 2014 [ BibTeX | DiVA ]
[25] J. A. Stork, F. T. Pokorny and D. Kragic. A topology-based object representation for clasping, latching and hooking. In 2013 13TH IEEE-RAS INTERNATIONAL CONFERENCE ON HUMANOID ROBOTS (HUMANOIDS), pages 138-145, 2013 [ BibTeX | DiVA ]
[26] F. T. Pokorny, J. A. Stork and D. Kragic. Grasping Objects with Holes : A Topological Approach. In 2013 IEEE International Conference on Robotics and Automation, pages 1100-1107, 2013 [ BibTeX | DiVA ]
[27] J. A. Stork, F. T. Pokorny and D. Kragic. Integrated motion and clasp planning with virtual linking. In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 3007-3014, 2013 [ BibTeX | DiVA ]
[28] J. A. Stork, F. T. Pokorny and D. Kragic. Towards Postural Synergies for Caging Grasps. 2013 [ BibTeX | DiVA ]
[29] J. A. Stork, L. Spinello, J. Silva and K. O. Arras. Audio-Based Human Activity Recognition Using Non-Markovian Ensemble Voting. In 2012 IEEE RO-MAN : The 21st IEEE International Symposium on Robot and Human Interactive Communication, pages 509-514, 2012 [ BibTeX | DiVA ]
[30] J. A. Stork, J. Silva, L. Spinello and K. O. Arras. Audio-Based Human Activity Recognition with Robots. 2011 [ BibTeX | DiVA ]
[31] M. Luber, J. A. Stork, G. D. Tipaldi and K. O. Arras. People tracking with human motion predictions from social forces. In 2010 IEEE International Conference on Robotics and Automation, Proceedings, pages 464-469, 2010 [ BibTeX | DiVA ]