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Jan Peters

75 papers · 2010–2026 · 12 conferences · across top CS/AI conferences

Achievements

πŸ—ΊοΈ Taxonomy Completionist (25) πŸƒ Academic Marathon (15) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌈 Renaissance Researcher (6) πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (12) 🐝 Cross-Pollinator (15) πŸ”¬ Deep Specialist (18) πŸ‘‘ Triple Crown 🧬 Topic Evolution πŸ† Keyword Champion (2) πŸ† Grand Slam πŸ‘₯ Mega-Team (22) 🀝 Dynamic Duo (12) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (12) πŸ—ƒοΈ Keyword Collector (81) πŸ’Ž Century Club (74) ⚑ Prolific Year (16)

Conferences

CORL (17) ICLR (13) ICML (11) JMLR (11) AISTATS (8) RSS (5) AAAI (3) IJCAI (2) L4DC (2) ACML (1) NIPS (1) UAI (1)

Top co-authors

Carlo D'Eramo (12) Davide Tateo (11) Boris Belousov (10) Gerhard Neumann (10) Puze Liu (8) Pascal Klink (7) Hany Abdulsamad (7) Joni Pajarinen (7) Michael Lutter (6) Marcello Restelli (5)

Research topics

Reinforcement Learning (1) Robotics (1)

Keywords

reinforcement learning (10) sample efficiency (5) policy learning (5) bayesian inference (5) policy search (5) trajectory optimization (4) relative entropy (4) model-based reinforcement learning (4) gaussian process (4) uncertainty quantification (3) sim-to-real transfer (3) continuous control (3) imitation learning (3) active learning (3) robot learning (3) online learning (2) expectation maximization (2) incremental learning (2) motion planning (2) optimal transport (2)

Papers

Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models (Abstract Reprint) AAAI 2026
Distilling Contact Planning for Fast Trajectory Optimization in Robot Air Hockey RSS 2025
Maximum Total Correlation Reinforcement Learning ICML 2025
DIME: Diffusion-Based Maximum Entropy Reinforcement Learning ICML 2025
Adaptive $Q$-Network: On-the-fly Target Selection for Deep Reinforcement Learning ICLR 2025
Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative Framework for Imitation Learning from Observation ICLR 2025
Towards Embodiment Scaling Laws in Robot Locomotion CORL 2025
Inverse decision-making using neural amortized Bayesian actors ICLR 2025
A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics NIPS 2024
PianoMime: Learning a Generalist, Dexterous Piano Player from Internet Demonstrations CORL 2024
Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning CORL 2024
One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion CORL 2024
Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning CORL 2024
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations AAAI 2024
Parameterized Projected Bellman Operator AAAI 2024
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model ACML 2024
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity ICLR 2024
Time-Efficient Reinforcement Learning with Stochastic Stateful Policies ICLR 2024
Domain Randomization via Entropy Maximization ICLR 2024
Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts ICLR 2024
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula ICLR 2024
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks ICML 2024
Reinforcement Learning for Athletic Intelligence: Lessons from the 1st β€œAI Olympics with RealAIGym” Competition IJCAI 2024
Value-Distributional Model-Based Reinforcement Learning JMLR 2024
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning ICLR 2023
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning ICLR 2023
Hierarchical Policy Blending As Optimal Transport L4DC 2023
Model-Based Uncertainty in Value Functions AISTATS 2023
Boosted Curriculum Reinforcement Learning ICLR 2022
Dimensionality Reduction and Prioritized Exploration for Policy Search AISTATS 2022
Curriculum Reinforcement Learning via Constrained Optimal Transport ICML 2022
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes CORL 2022
Robot Reinforcement Learning on the Constraint Manifold CORL 2021
Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction CORL 2021
Neural Posterior Domain Randomization CORL 2021
Gaussian Approximation for Bias Reduction in Q-Learning JMLR 2021
Robust Value Iteration for Continuous Control Tasks RSS 2021
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning RSS 2021
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning JMLR 2021
Convex Regularization in Monte-Carlo Tree Search ICML 2021
Value Iteration in Continuous Actions, States and Time ICML 2021
MushroomRL: Simplifying Reinforcement Learning Research JMLR 2021
Latent Derivative Bayesian Last Layer Networks AISTATS 2021
Sharing Knowledge in Multi-Task Deep Reinforcement Learning ICLR 2020
A Nonparametric Off-Policy Policy Gradient AISTATS 2020
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards CORL 2020
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation L4DC 2020
Bayesian Online Prediction of Change Points UAI 2020
Generalized Mean Estimation in Monte-Carlo Tree Search IJCAI 2020
Self-Paced Contextual Reinforcement Learning CORL 2019
Projections for Approximate Policy Iteration Algorithms ICML 2019
Switching Linear Dynamics for Variational Bayes Filtering ICML 2019
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning ICLR 2019
Receding Horizon Curiosity CORL 2019
Stochastic Optimal Control as Approximate Input Inference CORL 2019
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints CORL 2019
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos ICML 2018
Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment CORL 2018
Model-Free Trajectory-based Policy Optimization with Monotonic Improvement JMLR 2018
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling JMLR 2018
Local Bayesian Optimization of Motor Skills ICML 2017
Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals CORL 2017
Non-parametric Policy Search with Limited Information Loss JMLR 2017
Stability of Controllers for Gaussian Process Dynamics JMLR 2017
Active Incremental Learning of Robot Movement Primitives CORL 2017
Stability of Controllers for Gaussian Process Forward Models ICML 2016
Hierarchical Relative Entropy Policy Search JMLR 2016
Learning of Non-Parametric Control Policies with High-Dimensional State Features AISTATS 2015
Natural Evolution Strategies JMLR 2014
Policy Evaluation with Temporal Differences: A Survey and Comparison JMLR 2014
Active Reward Learning RSS 2014
Probabilistic Modeling of Human Movements for Intention Inference RSS 2012
Hierarchical Relative Entropy Policy Search AISTATS 2012
Relative Entropy Inverse Reinforcement Learning AISTATS 2011
Incremental Sparsification for Real-time Online Model Learning AISTATS 2010