Johannes A. Stork

About Johannes A. Stork

Johannes A. Stork, With an exceptional h-index of 19 and a recent h-index of 17 (since 2020), a distinguished researcher at Örebro Universitet, specializes in the field of Machine Learning, Artificial Intelligence, Robotics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Learning Extrinsic Dexterity with Parameterized Manipulation Primitives

Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer

Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning

Data-driven Grasping and Pre-grasp Manipulation Using Hierarchical Reinforcement Learning with Parameterized Action Primitives

Learn from Robot: Transferring Skills for Diverse Manipulation via Cycle Generative Networks

Hierarchical goals contextualize local reward decomposition explanations

Tracking Branched Deformable Linear Objects Using Particle Filtering on Depth Images

Learning differentiable dynamics models for shape control of deformable linear objects

Johannes A. Stork Information

University

Position

Assistant Professor

Citations(all)

1579

Citations(since 2020)

1162

Cited By

849

hIndex(all)

19

hIndex(since 2020)

17

i10Index(all)

27

i10Index(since 2020)

24

Email

University Profile Page

Google Scholar

Johannes A. Stork Skills & Research Interests

Machine Learning

Artificial Intelligence

Robotics

Top articles of Johannes A. Stork

Learning Extrinsic Dexterity with Parameterized Manipulation Primitives

arXiv preprint arXiv:2310.17785

2023/10/26

Diversity for Contingency: Learning Diverse Behaviors for Efficient Adaptation and Transfer

arXiv preprint arXiv:2310.07493

2023/10/11

Prioritized Soft Q-Decomposition for Lexicographic Reinforcement Learning

arXiv preprint arXiv:2310.02360

2023/10/3

Data-driven Grasping and Pre-grasp Manipulation Using Hierarchical Reinforcement Learning with Parameterized Action Primitives

2023/9/28

Learn from Robot: Transferring Skills for Diverse Manipulation via Cycle Generative Networks

2023/8/26

Hierarchical goals contextualize local reward decomposition explanations

Neural Computing and Applications

2023/8

Tracking Branched Deformable Linear Objects Using Particle Filtering on Depth Images

Available at SSRN 4531786

2023

Learning differentiable dynamics models for shape control of deformable linear objects

Robotics and Autonomous Systems

2022/12/1

Particle filters in latent space for robust deformable linear object tracking

IEEE Robotics and Automation Letters

2022/10/25

Online model learning for shape control of deformable linear objects

2022/10/23

Heterogeneous Full-body Control of a Mobile Manipulator with Behavior Trees

arXiv preprint arXiv:2210.08600

2022/10/16

A stack-of-tasks approach combined with behavior trees: A new framework for robot control

IEEE Robotics and Automation Letters

2022/10/3

Towards Task-Prioritized Policy Composition

arXiv preprint arXiv:2209.09536

2022/9/20

Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation

arXiv preprint arXiv:2210.02891

2022/9/19

Online distance field priors for gaussian process implicit surfaces

IEEE Robotics and Automation Letters

2022/7/8

Voting and attention-based pose relation learning for object pose estimation from 3D point clouds

IEEE Robotics and Automation Letters

2022/7/7

Variable impedance skill learning for contact-rich manipulation

IEEE Robotics and Automation Letters

2022/6/30

Mpr-rl: Multi-prior regularized reinforcement learning for knowledge transfer

IEEE Robotics and Automation Letters

2022/6/22

Context-aware grasp generation in cluttered scenes

2022/5/23

Learn to Predict Posterior Probability in Particle Filtering for Tracking Deformable Linear Objects

2022

See List of Professors in Johannes A. Stork University(Örebro Universitet)

Co-Authors

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