Jan Peters
77 papers · 2005–2026 · 13 venues · across top CS/AI venues
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Achievements
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(12)
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(5)
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(323)
Venues
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Research topics
Reinforcement Learning
(51)
Machine Learning
(47)
Methods
(41)
Artificial Intelligence
(37)
Core AI
(28)
Learning Types
(25)
Optimization & Theory
(25)
Applications
(24)
Deep Learning
(14)
Robotics
(14)
Capabilities
(10)
Bayesian & Probabilistic
(9)
Mathematics & Optimization
(7)
Optimization
(7)
Application Areas
(6)
Keywords
reinforcement learning
(15)
sample efficiency
(9)
imitation learning
(6)
continuous control
(6)
policy learning
(6)
bayesian inference
(6)
policy search
(5)
model-based reinforcement learning
(5)
trajectory optimization
(4)
value iteration
(4)
gaussian process
(4)
sim-to-real transfer
(4)
relative entropy
(4)
policy gradient
(3)
uncertainty quantification
(3)
deep reinforcement learning
(3)
inverse reinforcement learning
(3)
domain randomization
(3)
active learning
(3)
robot learning
(3)
Papers
Breaking the 3D Dataset Bottleneck: Fast Scalable Generation of Aligned 3D Assets from Scratch for Category 6D Pose Estimation and Robotic Grasping
CVPR 2026
Motion Planning Diffusion: Learning and Adapting Robot Motion Planning with Diffusion Models (Abstract Reprint)
AAAI 2026
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning
ICLR 2025
DIME: Diffusion-Based Maximum Entropy Reinforcement Learning
ICML 2025
Distilling Contact Planning for Fast Trajectory Optimization in Robot Air Hockey
RSS 2025
Inverse decision-making using neural amortized Bayesian actors
ICLR 2025
Maximum Total Correlation Reinforcement Learning
ICML 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
A Retrospective on the Robot Air Hockey Challenge: Benchmarking Robust, Reliable, and Safe Learning Techniques for Real-world Robotics
NeurIPS 2024
Bridging the gap between Learning-to-plan, Motion Primitives and Safe Reinforcement Learning
CoRL 2024
CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity
ICLR 2024
Domain Randomization via Entropy Maximization
ICLR 2024
Dude: Dual Distribution-Aware Context Prompt Learning For Large Vision-Language Model
ACML 2024
Handling Long-Term Safety and Uncertainty in Safe Reinforcement Learning
CoRL 2024
Multi-Task Reinforcement Learning with Mixture of Orthogonal Experts
ICLR 2024
One Policy to Run Them All: an End-to-end Learning Approach to Multi-Embodiment Locomotion
CoRL 2024
Parameterized Projected Bellman Operator
AAAI 2024
Peer Learning: Learning Complex Policies in Groups from Scratch via Action Recommendations
AAAI 2024
PianoMime: Learning a Generalist, Dexterous Piano Player from Internet Demonstrations
CoRL 2024
Reinforcement Learning for Athletic Intelligence: Lessons from the 1st βAI Olympics with RealAIGymβ Competition
IJCAI 2024
Robust Adversarial Reinforcement Learning via Bounded Rationality Curricula
ICLR 2024
Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks
ICML 2024
Time-Efficient Reinforcement Learning with Stochastic Stateful Policies
ICLR 2024
Value-Distributional Model-Based Reinforcement Learning
JMLR 2024
Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning
ICLR 2023
Hierarchical Policy Blending As Optimal Transport
L4DC 2023
LS-IQ: Implicit Reward Regularization for Inverse Reinforcement Learning
ICLR 2023
Model-Based Uncertainty in Value Functions
AISTATS 2023
Boosted Curriculum Reinforcement Learning
ICLR 2022
Curriculum Reinforcement Learning via Constrained Optimal Transport
ICML 2022
Dimensionality Reduction and Prioritized Exploration for Policy Search
AISTATS 2022
Inferring Smooth Control: Monte Carlo Posterior Policy Iteration with Gaussian Processes
CoRL 2022
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning
JMLR 2021
Composable Energy Policies for Reactive Motion Generation and Reinforcement Learning
RSS 2021
Convex Regularization in Monte-Carlo Tree Search
ICML 2021
Gaussian Approximation for Bias Reduction in Q-Learning
JMLR 2021
Latent Derivative Bayesian Last Layer Networks
AISTATS 2021
Learn2Assemble with Structured Representations and Search for Robotic Architectural Construction
CoRL 2021
MushroomRL: Simplifying Reinforcement Learning Research
JMLR 2021
Neural Posterior Domain Randomization
CoRL 2021
Robot Reinforcement Learning on the Constraint Manifold
CoRL 2021
Robust Value Iteration for Continuous Control Tasks
RSS 2021
Value Iteration in Continuous Actions, States and Time
ICML 2021
A Nonparametric Off-Policy Policy Gradient
AISTATS 2020
Bayesian Online Prediction of Change Points
UAI 2020
Generalized Mean Estimation in Monte-Carlo Tree Search
IJCAI 2020
Hierarchical Decomposition of Nonlinear Dynamics and Control for System Identification and Policy Distillation
L4DC 2020
High Acceleration Reinforcement Learning for Real-World Juggling with Binary Rewards
CoRL 2020
Sharing Knowledge in Multi-Task Deep Reinforcement Learning
ICLR 2020
Deep Lagrangian Networks: Using Physics as Model Prior for Deep Learning
ICLR 2019
HJB Optimal Feedback Control with Deep Differential Value Functions and Action Constraints
CoRL 2019
Projections for Approximate Policy Iteration Algorithms
ICML 2019
Receding Horizon Curiosity
CoRL 2019
Self-Paced Contextual Reinforcement Learning
CoRL 2019
Stochastic Optimal Control as Approximate Input Inference
CoRL 2019
Switching Linear Dynamics for Variational Bayes Filtering
ICML 2019
Domain Randomization for Simulation-Based Policy Optimization with Transferability Assessment
CoRL 2018
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling
JMLR 2018
Model-Free Trajectory-based Policy Optimization with Monotonic Improvement
JMLR 2018
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
ICML 2018
Active Incremental Learning of Robot Movement Primitives
CoRL 2017
Local Bayesian Optimization of Motor Skills
ICML 2017
Non-parametric Policy Search with Limited Information Loss
JMLR 2017
Online Learning with Stochastic Recurrent Neural Networks using Intrinsic Motivation Signals
CoRL 2017
Stability of Controllers for Gaussian Process Dynamics
JMLR 2017
Hierarchical Relative Entropy Policy Search
JMLR 2016
Stability of Controllers for Gaussian Process Forward Models
ICML 2016
Learning of Non-Parametric Control Policies with High-Dimensional State Features
AISTATS 2015
Active Reward Learning
RSS 2014
Natural Evolution Strategies
JMLR 2014
Policy Evaluation with Temporal Differences: A Survey and Comparison
JMLR 2014
Hierarchical Relative Entropy Policy Search
AISTATS 2012
Probabilistic Modeling of Human Movements for Intention Inference
RSS 2012
Relative Entropy Inverse Reinforcement Learning
AISTATS 2011
Incremental Sparsification for Real-time Online Model Learning
AISTATS 2010
An Expectation Maximization Algorithm for Continuous Markov Decision Processes with Arbitrary Reward
AISTATS 2005