Jens Kober - Code

Interactive Learning

Interactive Learning of Situational Awareness from Camera Input (ILeSiA)

Reference:

Petr Vanc, Giovanni Franzese, Jan Kristof Behrens, Cosimo Della Santina, Karla Štěpánová, Jens Kober, and Robert Babuška. ILeSiA: Interactive Learning of Situational Awareness from Camera Input. IEEE Robotics and Automation Letters, 10(10):10490–10497, 2025. [bibtex] [pdf] [webpage] [doi] [code] [video]

Active Skill-level Data Aggregation for Interactive Imitation Learning (ASkDAgger)

Reference:

Jelle Luijkx, Zlatan Ajanović, Laura Ferranti, and Jens Kober. ASkDAgger: Active Skill-level Data Aggregation for Interactive Imitation Learning. Transactions on Machine Learning Research, 2025. [bibtex] [pdf] [url] [webpage] [code] [video]

Learning Human-Aware Cooperation

Reference:

Linda F. van der Spaa, Jens Kober, and Michael Gienger. Simultaneously Learning Intentions and Preferences during Physical Human-Robot Cooperation. Autonomous Robots, 48(4):11, 2024. [bibtex] [pdf] [doi] [code] [video]

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, 29(5):4006 – 4018, 2024. [bibtex] [file] [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 Ramírez 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]

Minimum Uncertainty Dynamical System (MUDS)

Reference:

Anna Mészáros, Giovanni Franzese, and Jens Kober. Learning to Pick at Non-Zero-Velocity From Interactive Demonstrations. IEEE Robotics and Automation Letters, 7(3):6052–6059, 2022. [bibtex] [pdf] [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] [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 Proceedings of the 2018 International Symposium on Experimental Robotics (ISER 2018) (Jing Xiao, Torsten Kröger, Oussama Khatib, eds.), Springer International Publishing, pp. 353–363, 2020. [bibtex] [pdf] [doi] [code] [video]

(Deep) Reinforcement Learning

An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks

Reference:

Antonin Raffin, Olivier Sigaud, Jens Kober, Alin Albu-Schäffer, João Silvério, and Freek Stulp. An Open-Loop Baseline for Reinforcement Learning Locomotion Tasks. Reinforcement Learning Journal, 1(1), 2024. Reinforcement Learning Conference (RLC). [bibtex] [pdf] [html] [code] [video]

LLM-guided Task- and Affordance-Level Exploration (LLM-TALE),

Reference:

Jelle Luijkx, Runyu Ma, Zlatan Ajanović, and Jens Kober. LLM-Guided Task- and Affordance-Level Exploration in Reinforcement Learning. arXiv:2509.16615 [cs.RO], 2025. [bibtex] [pdf] [webpage] [doi] [code] [video]

Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization

Reference:

Daniel Jarne Ornia, Giannis Delimpaltadakis, Jens Kober, and Javier Alonso-Mora. Predictable Reinforcement Learning Dynamics through Entropy Rate Minimization. Transactions on Machine Learning Research, 2025. [bibtex] [pdf] [url] [code]

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]

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]

Jens Kober and Jan Peters. Imitation and Reinforcement Learning - Practical Algorithms for Motor Primitive Learning in Robotics. IEEE Robotics & Automation Magazine, 17(2):55–62, 2010. [bibtex] [pdf] [doi] [code] [video]

Jens Kober and Jan Peters. Learning Motor Primitives for Robotics. In IEEE International Conference on Robotics and Automation (ICRA), pp. 2112–2118, 2009. [bibtex] [pdf] [doi] [code] [video]

Jens Kober and Jan Peters. Policy Search for Motor Primitives in Robotics. In Advances in Neural Information Processing Systems 21 (NIPS) (D. Koller, D. Schuurmans, Y. Bengio, L. Bottou, eds.), Curran Associates, Inc., pp. 849–856, 2008. [bibtex] [pdf] [url] [code] [video]

Jens Kober. Reinforcement Learning for Motor Primitives. Master's thesis, University of Stuttgart, 2008. [bibtex] [file] [code] [video]

Simulation

Efficient Parallelized Simulation of Cyber-Physical Systems

Reference:

Bas van der Heijden, Laura Ferranti, Jens Kober, and Robert Babuška. Efficient Parallelized Simulation of Cyber-Physical Systems. Transactions on Machine Learning Research, 2024. Reproducibility Certification. [bibtex] [pdf] [url] [code] [video]

Robotic Environments with jaX (REX)

Reference:

Bas van der Heijden, Laura Ferranti, Jens Kober, and Robert Babuška. REX: GPU-Accelerated Sim2Real Framework with Delay and Dynamics Estimation. Transactions on Machine Learning Research, 2025. [bibtex] [pdf] [url] [code] [video]

Engine Agnostic Graph Environments for Robotics (EAGERx)

Reference:

Bas van der Heijden, Jelle Luijkx, Laura Ferranti, Jens Kober, and Robert Babuška. Engine Agnostic Graph Environments for Robotics (EAGERx): A Graph-Based Framework for Sim2real Robot Learning. IEEE Robotics & Automation Magazine, 32(2):99–112, 2025. [bibtex] [pdf] [webpage] [doi] [code] [video]

Imitation Learning

Noise-conditioned Energy-based Annealed Rewards (NEAR)

Reference:

Anish Abhijit Diwan, Julen Urain, Jens Kober, and Jan Peters. Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation. In 13^th International Conference on Learning Representations (ICLR), 2025. Jens Kober and Jan Peters supervised equally. [bibtex] [pdf] [webpage] [code] [video]

Policy via neUral Metric leArning (PUMA)

Reference:

Rodrigo Pérez-Dattari, Cosimo Della Santina, and Jens Kober. PUMA: Deep Metric Imitation Learning for Stable Motion Primitives. Advanced Intelligent Systems, 6(11):2400144, 2024. [bibtex] [file] [doi] [code] [video]

Gaussian Process Transportation (GPT)

Giovanni Franzese, Ravi Prakash, Cosimo Della Santina, and Jens Kober. Generalizable Motion Policies through Keypoint Parameterization and Transportation Maps. IEEE Transactions on Robotics, 41():4557–4573, 2025. [bibtex] [pdf] [doi] [code] [video]

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]

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]

Quadrupeds

Curriculum-Based Reinforcement Learning for Quadrupedal Jumping

Reference:

Vassil Atanassov, Jiatao Ding, Jens Kober, Ioannis Havoutis, and Cosimo Della Santina. Curriculum-Based Reinforcement Learning for Quadrupedal Jumping: A Reference-Free Design. IEEE Robotics & Automation Magazine, 32(2):35–48, 2025. [bibtex] [pdf] [doi] [code] [video]

Quadruped-Sim

Reference:

Francecso Vezzi, Jiatao Ding, Antonin Raffin, Jens Kober, and Cosimo Della Santina. Two-Stage Learning of Highly Dynamic Motions with Rigid and Articulated Soft Quadrupeds. In IEEE International Conference on Robotics and Automation (ICRA), pp. 9720–9726, 2024. [bibtex] [pdf] [doi] [code] [video]

Autonomous Vehicles

Graph-Based Motion Prediction (GMoP)

Reference:

Anna Mészáros, Javier Alonso-Mora, and Jens Kober. Studying the Effect of Explicit Interaction Representations on Learning Scene-Level Distributions of Human Trajectories. In IEEE Intelligent Vehicles Symposium (IV), 2026. Accepted. [bibtex] [pdf] [code] [data]

Structured Training and Evaluation Platform (STEP)

Reference:

Julian Frederik Schumann, Anna Mészáros, Jens Kober, and Arkady Zgonnikov. STEP: Structured Training and Evaluation Platform for benchmarking trajectory prediction models. arXiv:2509.14801 [cs.LG], 2025. [bibtex] [pdf] [doi] [code]

Mode Collapse Happens

Reference:

Maarten Hugenholtz, Anna Mészáros, Jens Kober, and Zlatan Ajanović. Mode Collapse Happens: Evaluating Critical Interactions in Joint Trajectory Prediction Models. arXiv:2506.23164 [cs.RO], 2025. [bibtex] [pdf] [doi] [code]

Learning Distributions over Trajectories for Human Behavior Prediction (TrajFlow)

Reference:

Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora, Arkady Zgonnikov, and Jens Kober. TrajFlow: Learning Distributions over Trajectories for Human Behavior Prediction. In IEEE Intelligent Vehicles Symposium (IV), pp. 184–191, 2024. [bibtex] [pdf] [doi] [code]

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), pp. 5870–5875, 2023. [bibtex] [pdf] [doi] [code]

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]

Wind-turbine Control

Output-constrained IPC: Trading Of Actuation Effort and Blade Fatigue Load Reduction

Reference:

Jesse Ishi Storm Hummel, Jens Kober, and Sebastiaan Paul Mulders. Output-Constrained Individual Pitch Control Methods Using the Multiblade Coordinate Transformation: Trading Off Actuation Effort and Blade Fatigue Load Reduction for Wind Turbines. Wind Energy Science, 10(9):2005–2023, 2025. [bibtex] [pdf] [url] [doi] [code]

Output-Constrained Individual Pitch Control using an Adaptive Leaky Integrator for Wind Turbine Blade Load Reductions

Reference:

Jesse Ishi Storm Hummel, Jens Kober, and Sebastiaan Paul Mulders. Output-Constrained Individual Pitch Control using an Adaptive Leaky Integrator for Wind Turbine Blade Load Reductions. In American Control Conference (ACC), pp. 2836-2841, 2025. [bibtex] [doi] [code]

Others

Minimalist and User-friendly Kinematics Calibration (MUKCa)

Reference:

Giovanni Franzese, Max Spahn, Jens Kober, and Cosimo Della Santina. MUKCa: Accurate and Affordable Cobot Calibration Without External Measurement Devices. arXiv:2503.12584 [cs.RO], 2025. [bibtex] [pdf] [doi] [code] [video]

Robust Multi-Modal Density Estimation (ROME)

Reference:

Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora, Arkady Zgonnikov, and Jens Kober. ROME: Robust Multi-Modal Density Estimation. In Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI) (Kate Larson, ed.), International Joint Conferences on Artificial Intelligence Organization, pp. 4751–4759, 2024. Main Track. [bibtex] [pdf] [doi] [code] [data]

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]

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]

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]