Jens Kober

Interactive Learning

Safe Interactive Movement Primitive Learning (SIMPLe)

Reference:

Giovanni Franzese, Leandro de Souza Rosa, Tim Verburg, Luka Peternel, and Jens Kober. Interactive Imitation Learning of Bimanual Movement Primitives. IEEE/ASME Transactions on Mechatronics, ():1–13, 2023. [bibtex] [doi] [code] [video]

Interactive Corrections and Reinforcements for an Epistemic and Aleatoric uncertainty-aware Teaching (ICREATe)

Reference:

Carlos E. Celemin and Jens Kober. Knowledge- and Ambiguity-Aware Robot Learning from Corrective and Evaluative Feedback. Neural Computing and Applications, 35(23):16821–16839, 2023. [bibtex] [pdf] [doi] [code] [video]

Combining Interactive Teaching and Self-Exploration

Reference:

Mariano Ramirez Montero, Giovanni Franzese, Jeroen Zwanepol, and Jens Kober. Solving Robot Assembly Tasks by Combining Interactive Teaching and Self-Exploration. arXiv:2209.11530 [cs.RO], 2022. [bibtex] [pdf] [doi] [code] [video]

Social Model Predictive Contouring Control (Social-MPCC)

Reference:

Rodrigo Pérez-Dattari, Bruno Brito, Oscar de Groot, Jens Kober, and Javier Alonso-Mora. Visually-Guided Motion Planning for Autonomous Driving from Interactive Demonstrations. Engineering Applications of Artificial Intelligence, 116:105277, 2022. [bibtex] [url] [doi] [code] [video]

Interactive Learning of Stiffness and Attractors (ILoSA)

Reference:

Giovanni Franzese, Anna Mészáros, Luka Peternel, and Jens Kober. ILoSA: Interactive Learning of Stiffness and Attractors. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7778–7785, 2021. [bibtex] [pdf] [url] [doi] [code] [video]

Learning Interactively to Resolve Ambiguity (LIRA)

Reference:

Giovanni Franzese, Carlos E. Celemin, and Jens Kober. Learning Interactively to Resolve Ambiguity in Reference Frame Selection. In 2020 Conference on Robot Learning (CoRL) (Jens Kober, Fabio Ramos, Claire Tomlin, eds.), PMLR, vol. 155 of Proceedings of Machine Learning Research, pp. 1298–1311, 2021. [bibtex] [pdf] [html] [code] [video]

Teaching Imitative Policies in State-space (TIPS)

Reference:

Snehal Jauhri, Carlos E. Celemin, and Jens Kober. Interactive Imitation Learning in State-Space. In 2020 Conference on Robot Learning (CoRL) (Jens Kober, Fabio Ramos, Claire Tomlin, eds.), PMLR, vol. 155 of Proceedings of Machine Learning Research, pp. 682–692, 2021. [bibtex] [pdf] [html] [code] [video]

Interactive Learning of Temporal Features for Control

Reference:

Rodrigo Pérez-Dattari, Carlos E. Celemin, Giovanni Franzese, Javier Ruiz-del-Solar, and Jens Kober. Interactive Learning of Temporal Features for Control: Shaping Policies and State Representations From Human Feedback. IEEE Robotics & Automation Magazine, 27(2):46–54, 2020. [bibtex] [pdf] [doi] [code] [video]

Predictive Probabilistic Merging of Policies (PPMP)

Reference:

Jan Scholten, Daan Wout, Carlos E. Celemin, and Jens Kober. Deep Reinforcement Learning with Feedback-based Exploration. In IEEE Conference on Decision and Control (CDC), pp. 803–808, 2019. [bibtex] [pdf] [doi] [code]

Gaussian Process Coach (GPC)

Reference:

Daan Wout, Jan Scholten, Carlos E. Celemin, and Jens Kober. Learning Gaussian Policies from Corrective Human Feedback. arXiv:1903.05216 [cs.LG], 2019. [bibtex] [pdf] [doi] [code]

Enhanced Deep COACH (enhanced D-COACH)

Reference:

Rodrigo Pérez-Dattari, Carlos E. Celemin, Javier Ruiz-del-Solar, and Jens Kober. Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach. In IEEE International Conference on Robotics and Automation (ICRA), pp. 7611–7617, 2019. [bibtex] [pdf] [doi] [code] [video]

Deep COACH (D-COACH)

Reference:

Rodrigo Pérez-Dattari, Carlos E. Celemin, Javier Ruiz-del-Solar, and Jens Kober. Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks. In International Symposium on Experimental Robotics (ISER) (Jing Xiao, Torsten Kröger, Oussama Khatib, eds.), Springer International Publishing, pp. 353–363, 2018. [bibtex] [pdf] [doi] [code] [video]

Deep Reinforcement Learning

Fine-tuning Deep RL with Gradient-Free Optimization

Reference:

Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška. Fine-tuning Deep RL with Gradient-Free Optimization. In 21^th IFAC World Congress, pp. 8049–8056, 2020. [bibtex] [pdf] [doi] [code]

Experience Selection Baselines

Reference:

Tim de Bruin, Jens Kober, Karl Tuyls, and Robert Babuška. Experience Selection in Deep Reinforcement Learning for Control. Journal of Machine Learning Research, 19(9):1–56, 2018. [bibtex] [pdf] [html] [code] [video]

Others

Evaluating the Commotions model

Reference:

Julian F. Schumann, Aravinda Ramakrishnan Srinivasan, Jens Kober, Gustav Markkula, and Arkady Zgonnikov. Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study. In IEEE 26^th International Conference on Intelligent Transportation Systems (ITSC), 2023. [bibtex] [pdf] [code]

CONvergent Dynamics from demOnstRations (CONDOR)

Reference:

Rodrigo Pérez-Dattari and Jens Kober. Stable Motion Primitives via Imitation and Contrastive Learning. IEEE Transactions on Robotics, ():1–20, 2023. [bibtex] [pdf] [doi] [code] [video]

Framework for Benchmarking Gap Acceptance

Reference:

Julian Frederik Schumann, Jens Kober, and Arkady Zgonnikov. Benchmarking Behavior Prediction Models in Gap Acceptance Scenarios. IEEE Transactions on Intelligent Vehicles, 8(3):2580–2591, 2023. [bibtex] [pdf] [doi] [code]

Disagreement-Aware Variable Impedance Control (DAVI)

Reference:

Linda van der Spaa, Giovanni Franzese, Jens Kober, and Michael Gienger. Disagreement-Aware Variable Impedance Control for Online Learning of Physical Human-Robot Cooperation Tasks. In ICRA 2022 full day workshop - Shared Autonomy in Physical Human-Robot Interaction: Adaptability and Trust, 2022. [bibtex] [pdf] [url] [code] [video]

Learning Task-Parameterzied Skills from Few Demonstrations

Reference:

Jihong Zhu, Michael Gienger, and Jens Kober. Learning Task-Parameterized Skills from Few Demonstrations. IEEE Robotics and Automation Letters, 7(2):4063–4070, 2022. The contents of this paper were also selected by ICRA'22 Program Committee for presentation at the Conference. [bibtex] [pdf] [webpage] [doi] [code] [video]

Random Shadows and Highlights

Reference:

Osama Mazhar and Jens Kober. Random Shadows and Highlights: A New Data Augmentation Method for Extreme Lighting Conditions. arXiv:2101.05361 [cs.CV], 2021. [bibtex] [pdf] [doi] [code]

Policy learning by Weighting Exploration with the Returns (PoWER)

A basic MATLAB/Octave implementation of Policy learning by Weighting Exploration with the Returns (PoWER) [4 variants: return or state-action value function, constant exploration or automatic adaptation], episodic Reward-Weighted Regression (eRWR), episodic Natural Actor Critic (eNAC), ‘Vanilla’ Policy Gradients (VPG), and Finite Difference Gradients (FDG): matlab_PoWER.zip

The required motor primitive code can be downloaded from ~~http://www-clmc.usc.edu/Resources/Software~~ backup April 26, 2020

Reference:

Jens Kober and Jan Peters. Policy Search for Motor Primitives in Robotics. Machine Learning, 84(1-2):171–203, 2011. [bibtex] [pdf] [doi] [code] [video]

DMPs for hitting and batting

A basic MATLAB/Octave implementation: hittingMP.m

Reference:

Jens Kober, Katharina Muelling, Oliver Kroemer, Christoph H. Lampert, Bernhard Schölkopf, and Jan Peters. Movement Templates for Learning of Hitting and Batting. In IEEE International Conference on Robotics and Automation (ICRA), pp. 69–82, 2010. [bibtex] [pdf] [doi] [code] [video]

Interactive Learning

Safe Interactive Movement Primitive Learning (SIMPLe)

Reference:

Interactive Corrections and Reinforcements for an Epistemic and Aleatoric uncertainty-aware Teaching (ICREATe)

Reference:

Combining Interactive Teaching and Self-Exploration

Reference:

Social Model Predictive Contouring Control (Social-MPCC)

Reference:

Interactive Learning of Stiffness and Attractors (ILoSA)

Reference:

Learning Interactively to Resolve Ambiguity (LIRA)

Reference:

Teaching Imitative Policies in State-space (TIPS)

Reference:

Interactive Learning of Temporal Features for Control

Reference:

Predictive Probabilistic Merging of Policies (PPMP)

Reference:

Gaussian Process Coach (GPC)

Reference:

Enhanced Deep COACH (enhanced D-COACH)

Reference:

Deep COACH (D-COACH)

Reference:

Deep Reinforcement Learning

Fine-tuning Deep RL with Gradient-Free Optimization

Reference:

Experience Selection Baselines

Reference:

Others

Evaluating the Commotions model

Reference:

CONvergent Dynamics from demOnstRations (CONDOR)

Reference:

Framework for Benchmarking Gap Acceptance

Reference:

Disagreement-Aware Variable Impedance Control (DAVI)

Reference:

Learning Task-Parameterzied Skills from Few Demonstrations

Reference:

Random Shadows and Highlights

Reference:

Policy learning by Weighting Exploration with the Returns (PoWER)

Reference:

DMPs for hitting and batting

Reference:

Work:

Private: