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] gold open access

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] gold open access

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] bronze open access

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] gold open access

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] green open access

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] gold open access

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] gold open access

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] green open access

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] green open access

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] bronze open access

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] green open access

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] green open access

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 21th IFAC World Congress, pp. 8049–8056, 2020. [bibtex] [pdf] [doi] [code] gold open access

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] gold open access

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 26th International Conference on Intelligent Transportation Systems (ITSC), 2023. [bibtex] [pdf] [code] green open access

CONvergent Dynamics from demOnstRations (CONDOR)

Reference:

Rodrigo Pérez-Dattari and Jens Kober. Stable Motion Primitives via Imitation and Contrastive Learning. IEEE Transactions on Robotics, 39(5):3909–3928, 2023. [bibtex] [pdf] [doi] [code] [video] green open access

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] green open access

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] bronze open access

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] green open access

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] bronze open access

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] bronze open access

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] green open access