Example

Research Interests

Robot Learning, Robotics, Machine Learning, Cognitive Science and Biomimetic Systems.

Affiliations

1. TU Darmstadt, Intelligent Autonomous Systems, Computer Science Department
2. German Research Center for AI (DFKI), Research Department: SAIROL
3. Hessian Centre for Artificial Intelligence

Contact

myname@ias.tu-darmstadt.de
Room E315, Building S2|02, TU Darmstadt, FB-Informatik, FG-IAS, Hochschulstr. 10, 64289 Darmstadt
+49-6151-16-00000

About Me

I'm [Your Name], a passionate [Your Profession/Interest] based in [Your Location]. Welcome to my corner of the internet, where I share my thoughts, experiences, and interests with the world. I believe in [Your Belief/Philosophy], and I'm dedicated to [Your Goal/Purpose].

My Journey

My journey began [Start Date/Year], and it's been an incredible adventure ever since. I've had the privilege of [Highlight One or Two Significant Achievements or Experiences], and I continue to strive for excellence in everything I do.

My Interests

I have a wide range of interests, including [List 3-5 Interests / Hobbies]. These passions fuel my creativity and drive my desire to learn and grow every day.

Blog

On my blog, you'll find articles and insights on a variety of topics. I love sharing my knowledge and experiences with my readers. Whether it's [Choose a few Topics, e.g., Travel, Technology, Cooking], I aim to provide valuable content that informs and inspires.

Portfolio

Browse through my portfolio to see some of my [Work/Projects/Creations]. I'm proud of what I've accomplished so far, and I'm always open to new opportunities and collaborations.

Get in Touch I'm a firm believer in the power of connections. If you'd like to collaborate, have questions, or just want to say hello, don't hesitate to [Provide Contact Information/Links to Social Media].

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Thank you for visiting my personal homepage. I hope you find something here that resonates with you or sparks your curiosity. Feel free to explore, connect, and join me on this exciting journey!

Publications

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    Abdulsamad, H.; Nickl, P.; Klink, P.; Peters, J. (2024). Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 46, 4, pp.1950-1963.
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    Kicki, P.; Liu, P.; Tateo, D.; Bou Ammar, H.; Walas, K.; Skrzypczynski, P.; Peters, J. (2024). Fast Kinodynamic Planning on the Constraint Manifold with Deep Neural Networks, IEEE Transactions on Robotics (T-Ro), and Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), 40, pp.277-297.
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    Bhatt, A.; Palenicek, D.; Belousov, B.; Argus, M.; Amiranashvili, A.; Brox, T.; Peters, J. (2024). CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity, International Conference on Learning Representations (ICLR), Spotlight.
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    Derstroff, C.; Brugger, J.; Cerrato, M.; Peters, J.; Kramer, S. (2024). Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations, Proceedings of the National Conference on Artificial Intelligence (AAAI).
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    Vincent, T.; Metelli, A.; Belousov, B.; Peters, J.; Restelli, M.; D'Eramo, C. (2024). Parameterized Projected Bellman Operator, Proceedings of the National Conference on Artificial Intelligence (AAAI).
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    Tiboni, G.; Klink, P.; Peters, J.; Tommasi, T.; D'Eramo, C.; Chalvatzaki, G. (2024). Domain Randomization via Entropy Maximization, International Conference on Learning Representations (ICLR).
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    Goeksu, Y.; Almeida-Correia, A.; Prasad, V.; Kshirsagar, A.; Koert, D.; Peters, J.; Chalvatzaki, G. (2024). Kinematically Constrained Human-like Bimanual Robot-to-Human Handovers, ACM/IEEE International Conference on Human Robot Interaction (HRI), Late Breaking Report.
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    Hendawy, A.; Peters, J.; D'Eramo, C. (2024). Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts, International Conference on Learning Representations (ICLR).
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    Reddi, A.; Toelle, M.; Peters, J.; Chalvatzaki, G.; D'Eramo, C. (2024). Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula, International Conference on Learning Representations (ICLR).
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    Boehm, A.; Schneider, T.; Belousov, B.; Kshirsagar, A.; Lin, L.; Doerschner, K.; Drewing, K.; Rothkopf, C.A.; Peters, J. (2024). What Matters for Active Texture Recognition With Vision-Based Tactile Sensors, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
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    Lach, L.; Haschke, R.; Tateo, D.; Peters, J.; Ritter, H.; Sol, J.; Torras, C. (2024). Transferring Tactile-based Continuous Force Control Policies from Simulation to Robot, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
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    Spartakov, R.; Kshirsagar, A.; Mühl, D.; Schween, R.; Endres, D.M.; Bremmer, F.; Melzig, C.; Peters, J. (2024). Balancing on the Edge: Review and Computational Framework on the Dynamics of Fear of Falling and Fear of Heights in Postural Control, Proceedings of the Annual Meeting of the Cognitive Science Society (CogSci).
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    Holzmann, P.; Maik Pfefferkorn, M.; Peters, J.; Findeisen, R. (2024). Learning Energy-Efficient Trajectory Planning for Robotic Manipulators using Bayesian Optimization, Proceedings of the European Control Conference (ECC).
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    Wiebe, F.; Turcato, N.; Dalla Libera, A.; Zhang, C.; Vincent, T.; Vyas, S.; Giacomuzzo, G.; Carli, R.; Romeres, D.; Sathuluri, A.; Zimmermann, M.; Belousov, B.; Peters, J.; Kirchner, F.; Kumar, S. (2024). Reinforcement Learning for Athletic Intelligence: Lessons from the 1st “AI Olympics with RealAIGym” Competition, The 33rd International Joint Conference on Artificial Intelligence.
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    Lin, L.; Boehm, A.; Belousov, B.; Kshirsagar, A.; Schneider, T.; Peters, J. Doerschner, K.; Drewing, K. (2024). Task-Adapted Single-Finger Explorations of Complex Objects, Eurohaptics.
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    Buechler, D.; Calandra, R.; Peters, J. (2023). Learning to Control Highly Accelerated Ballistic Movements on Muscular Robots, Robotics and Autonomous Systems, 159, 104230.
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    Toelle, M.; Belousov, B.; Peters, J. (2023). A Unifying Perspective on Language-Based Task Representations for Robot Control, CoRL Workshop on Language and Robot Learning: Language as Grounding.
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    Lutter, M.; Peters, J. (2023). Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models, International Journal of Robotics Research (IJRR), 42, 3.
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    Lutter, M.; Belousov, B.; Mannor, S.; Fox, D.; Garg, A.; Peters, J. (2023). Continuous-Time Fitted Value Iteration for Robust Policies, IEEE Transaction on Pattern Analysis and Machine Intelligence (PAMI).
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    Loeckel, S.; Ju, S.; Schaller, M.; van Vliet, P..; Peters, J. (2023). An Adaptive Human Driver Model for Realistic Race Car Simulations, IEEE Transactions on Systems, Man and Cybernetics: Systems (TSMC), 53, 11, pp.6718-6730.
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    Look, A.; Kandemir, M.; Rakitsch, B.; Peters, J. (2023). A Deterministic Approximation to Neural SDEs, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 45, 4, pp.4023-4037.
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    Liu, Y.; Belousov, B.; Funk, N.; Chalvatzaki, G.; Peters, J.; Tessman, O. (2023). Auto(mated)nomous Assembly, International Conference on Trends on Construction in the Post-Digital Era, pp.167-181, Springer, Cham.
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    Liu, P.; Zhang, K.; Tateo, D.; Jauhri, S.; Hu, Z.; Peters, J. Chalvatzaki, G. (2023). Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction, 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.
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    Zhu, Y.; Nazirjonov, S.; Jiang, B.; Colan, J.; Aoyama, T.; Hasegawa, Y.; Belousov, B.; Hansel, K.; Peters, J. (2023). Visual Tactile Sensor Based Force Estimation for Position-Force Teleoperation, IEEE International Conference on Cyborg and Bionic Systems (CBS), pp.49-52.
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    Zelch, C.; Peters, J.; von Stryk, C. (2023). Start State Selection for Control Policy Learning from Optimal Trajectories, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA).
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    Urain, J.; Funk, N.; Peters, J.; Chalvatzaki G (2023). SE(3)-DiffusionFields: Learning smooth cost functions for joint grasp and motion optimization through diffusion, International Conference on Robotics and Automation (ICRA).
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    Hansel, K.; Urain, J.; Peters, J.; Chalvatzaki, G. (2023). Hierarchical Policy Blending as Inference for Reactive Robot Control, 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE.
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    Le, A. T.; Hansel, K.; Peters, J.; Chalvatzaki, G. (2023). Hierarchical Policy Blending As Optimal Transport, 5th Annual Learning for Dynamics & Control Conference (L4DC), PMLR.
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    Luis, C.; Bottero, A.G.; Vinogradska, J.; Berkenkamp, F.; Peters, J. (2023). Model-Based Uncertainty in Value Functions, Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS).
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    Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning, International Conference on Learning Representations (ICLR).
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    Palenicek, D.; Lutter, M.; Carvalho, J.; Peters, J. (2023). Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning, International Conference on Learning Representations (ICLR).
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    Buechler, D.; Guist, S.; Calandra, R.; Berenz, V.; Schoelkopf, B.; Peters, J. (2023). Learning to Play Table Tennis From Scratch using Muscular Robots, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE T-TRo Track.
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    Urain, J.; Tateo, D.; Peters, J. (2023). Learning Stable Vector Fields on Lie Groups, Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), IEEE R-AL Track.
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    Bjelonic, F.; Lee, J.; Arm, P.; Sako, D.; Tateo, D.; Peters, J.; Hutter, M. (2023). Learning-Based Design and Control for Quadrupedal Robots With Parallel-Elastic Actuators, IEEE Robotics and Automation Letters (R-AL), 8, 3, pp.1611-1618.
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    Bethge, J.; Pfefferkorn, M.; Rose, A.; Peters, J.; Findeisen, R. (2023). Model predictive control with Gaussian-process-supported dynamical constraints for autonomous vehicles, Proceedings of the 22nd World Congress of the International Federation of Automatic Control.
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    Urain, J.; Li, A.; Liu, P.; D'Eramo, C.; Peters, J. (2023). Composable energy policies for reactive motion generation and reinforcement learning, International Journal of Robotics Research (IJRR).
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    Ju, S.; van Vliet, P.; Arenz, O.; Peters, J. (2023). Digital Twin of a Driver-in-the-Loop Race Car Simulation with Contextual Reinforcement Learning, IEEE Robotics and Automation Letters (RA-L), 8, 7, pp.4107-4114.
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    Peters, S.; Peters, J.; Findeisen, R. (2023). Quantifying Uncertainties along the Automated Driving Stack, ATZ worldwide volume, 125, pp.62-65.
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    Carvalho, J.; Le, A. T.; Baierl, M.; Koert, D.; Peters, J. (2023). Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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    Funk, N.; Mueller, P.-O.; Belousov, B.; Savchenko, A.; Findeisen, R.; Peters, J. (2023). High-Resolution Pixelwise Contact Area and Normal Force Estimation for the GelSight Mini Visuotactile Sensor Using Neural Networks, Embracing Contacts-Workshop at ICRA 2023.
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    Vincent, T.; Belousov, B.; D'Eramo, C.; Peters, J. (2023). Iterated Deep Q-Network: Efficient Learning of Bellman Iterations for Deep Reinforcement Learning, European Workshop on Reinforcement Learning (EWRL).
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    Al-Hafez, F.; Tateo, D.; Arenz, O.; Zhao, G.; Peters, J. (2023). Least Squares Inverse Q-Learning, European Workshop on Reinforcement Learning (EWRL).
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    Look, A.; Kandemir, M.; Rakitsch, B.; Peters, J. (2023). Cheap and Deterministic Inference for Deep State-Space Models of Interacting Dynamical Systems, Transactions on Machine Learning Research (TMLR).
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    Flynn, H.; Reeb, D.; Kandemir, M.; Peters, J. (2023). Improved Algorithms for Stochastic Linear Bandits Using Tail Bounds for Martingale Mixtures, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
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    Gruner, T.; Belousov, B.; Muratore, F.; Palenicek, D.; Peters, J. (2023). Pseudo-Likelihood Inference, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
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    Le, A. T.; Chalvatzaki, G.; Biess, A.; Peters, J. (2023). Accelerating Motion Planning via Optimal Transport, Advances in Neural Information Processing Systems (NIPS / NeurIPS).
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    Le, A. T.; Chalvatzaki, G.; Biess, A.; Peters, J. (2023). Accelerating Motion Planning via Optimal Transport, IROS 2023 Workshop on Differentiable Probabilistic Robotics: Emerging Perspectives on Robot Learning, [Oral].
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    Rother, D.; Weisswange, T.H.; Peters, J. (2023). Disentangling Interaction using Maximum Entropy Reinforcement Learning in Multi-Agent Systems, European Conference on Artificial Intelligence (ECAI).
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    Vincent, T.; Metelli, A.; Peters, J.; Restelli, M.; D'Eramo, C. (2023). Parameterized projected Bellman operator, ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems.
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    Metternich, H.; Hendawy, A.; Klink, P.; Peters, J.; D'Eramo, C. (2023). Using Proto-Value Functions for Curriculum Generation in Goal-Conditioned RL, NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning.
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    Le, A. T.; Chalvatzaki, G.; Biess, A.; Peters, J. (2023). Accelerating Motion Planning via Optimal Transport, NeurIPS 2023 Workshop Optimal Transport and Machine Learning, [Oral].
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    Boehm, A.; Schneider, T.; Belousov, B.; Kshirsagar, A.; Lin, L.; Doerschner, K.; Drewing, K.; Rothkopf, C.A.; Peters, J. (2023). Tactile Active Texture Recognition With Vision-Based Tactile Sensors, NeurIPS Workshop on Touch Processing: a new Sensing Modality for AI.
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    Watson, J.; Peters, J.; (2023). Sample-Efficient Online Imitation Learning using Pretrained Behavioural Cloning Policies, NeurIPS 6th Robot Learning Workshop: Pretraining, Fine-Tuning, and Generalization with Large Scale Models.
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    Al-Hafez, F.; Zhao, G.; Peters, J.; Tateo, D. (2023). LocoMuJoCo: A Comprehensive Imitation Learning Benchmark for Locomotion, Robot Learning Workshop, Conference on Neural Information Processing Systems (NeurIPS).
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    Zelch, C.; Peters, J.; von Stryk, O. (2023). Clustering of Motion Trajectories by a Distance Measure Based on Semantic Features, Proceedings of the IEEE International Conference on Humanoid Robots (Humanoids).
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    Parisi, S.; Tateo, D.; Hensel, M.; D'Eramo, C.; Peters, J.; Pajarinen, J. (2022). Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning, Algorithms, 15, 3, pp.81.
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    Akrour, R.; Tateo, D.; Peters, J. (2022). Continuous Action Reinforcement Learning from a Mixture of Interpretable Experts, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 44, 10, pp.6795-6806.
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    Loeckel, S.; Kretschi, A.; van Vliet, P.; Peters, J. (2022). Identification and modelling of race driving styles, Vehicle System Dynamics, 60, 8, pp.2890--2918.
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    Tosatto, S.; Carvalho, J.; Peters, J. (2022). Batch Reinforcement Learning with a Nonparametric Off-Policy Policy Gradient, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 44, 10, pp.5996--6010.
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    Belousov, B.; Wibranek, B.; Schneider, J.; Schneider, T.; Chalvatzaki, G.; Peters, J.; Tessmann, O. (2022). Robotic Architectural Assembly with Tactile Skills: Simulation and Optimization, Automation in Construction, 133, pp.104006.