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2020 – today
- 2023
[j16]Mikko Lauri
, David Hsu, Joni Pajarinen:
Partially Observable Markov Decision Processes in Robotics: A Survey. IEEE Trans. Robotics 39(1): 21-40 (2023)- [j15]Joni Pajarinen, Jens Lundell, Ville Kyrki:
POMDP Planning Under Object Composition Uncertainty: Application to Robotic Manipulation. IEEE Trans. Robotics 39(1): 41-56 (2023) - [i33]Kalle Kujanpää, Joni Pajarinen, Alexander Ilin:
Hierarchical Imitation Learning with Vector Quantized Models. CoRR abs/2301.12962 (2023) - [i32]Wenyan Yang, Joni Pajarinen, Dinging Cai, Joni-Kristian Kämäräinen:
Prioritized offline Goal-swapping Experience Replay. CoRR abs/2302.07741 (2023) - [i31]Wenyan Yang, Huiling Wang, Dingding Cai, Joni Pajarinen, Joni-Kristian Kämäräinen:
Swapped goal-conditioned offline reinforcement learning. CoRR abs/2302.08865 (2023) - [i30]Wenyan Yang, Alexandre Angleraud, Roel S. Pieters, Joni Pajarinen, Joni-Kristian Kämäräinen:
Seq2Seq Imitation Learning for Tactile Feedback-based Manipulation. CoRR abs/2303.02646 (2023) - 2022
- [j14]Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Long-Term Visitation Value for Deep Exploration in Sparse-Reward Reinforcement Learning. Algorithms 15(3): 81 (2022) - [j13]Nataliya Strokina, Wenyan Yang, Joni Pajarinen, Nikolay Serbenyuk, Joni-Kristian Kämäräinen, Reza Ghabcheloo:
Visual Rewards From Observation for Sequential Tasks: Autonomous Pile Loading. Frontiers Robotics AI 9: 838059 (2022) - [j12]Abdolreza Taheri, Pelle Gustafsson, Marcus Rösth, Reza Ghabcheloo, Joni Pajarinen:
Nonlinear Model Learning for Compensation and Feedforward Control of Real-World Hydraulic Actuators Using Gaussian Processes. IEEE Robotics Autom. Lett. 7(4): 9525-9532 (2022) - [j11]Tuan Dam, Georgia Chalvatzaki, Jan Peters, Joni Pajarinen:
Monte-Carlo Robot Path Planning. IEEE Robotics Autom. Lett. 7(4): 11213-11220 (2022) - [c29]Yi Zhao, Rinu Boney, Alexander Ilin, Juho Kannala, Joni Pajarinen:
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning. ESANN 2022 - [c28]Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal:
Topological Experience Replay. ICLR 2022 - [c27]Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Boosted Curriculum Reinforcement Learning. ICLR 2022 - [c26]Pascal Klink, Haoyi Yang, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Curriculum Reinforcement Learning via Constrained Optimal Transport. ICML 2022: 11341-11358 - [c25]Abdolreza Taheri, Joni Pajarinen, Reza Ghabcheloo:
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control. ICRA 2022: 10557-10563 - [c24]Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal:
Redeeming intrinsic rewards via constrained optimization. NeurIPS 2022 - [i29]Vivienne Huiling Wang, Joni Pajarinen, Tinghuai Wang, Joni-Kristian Kämäräinen:
Hierarchical Reinforcement Learning with Adversarially Guided Subgoals. CoRR abs/2201.09635 (2022) - [i28]Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search. CoRR abs/2202.07071 (2022) - [i27]Abdolreza Taheri, Joni Pajarinen, Reza Ghabcheloo:
GPU-Accelerated Policy Optimization via Batch Automatic Differentiation of Gaussian Processes for Real-World Control. CoRR abs/2202.13638 (2022) - [i26]Zhang-Wei Hong, Tao Chen, Yen-Chen Lin, Joni Pajarinen, Pulkit Agrawal:
Topological Experience Replay. CoRR abs/2203.15845 (2022) - [i25]Wenshuai Zhao, Joni Pajarinen:
Self-Paced Multi-Agent Reinforcement Learning. CoRR abs/2205.10016 (2022) - [i24]Tuan Dam, Georgia Chalvatzaki, Jan Peters, Joni Pajarinen:
Monte-Carlo Robot Path Planning. CoRR abs/2208.02673 (2022) - [i23]Mikko Lauri, David Hsu, Joni Pajarinen:
Partially Observable Markov Decision Processes in Robotics: A Survey. CoRR abs/2209.10342 (2022) - [i22]Amin Babadi, Yi Zhao, Juho Kannala, Alexander Ilin, Joni Pajarinen:
Continuous Monte Carlo Graph Search. CoRR abs/2210.01426 (2022) - [i21]Yi Zhao, Rinu Boney, Alexander Ilin, Juho Kannala, Joni Pajarinen:
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning. CoRR abs/2210.13846 (2022) - [i20]Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal:
Redeeming Intrinsic Rewards via Constrained Optimization. CoRR abs/2211.07627 (2022) - 2021
- [j10]Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning. J. Mach. Learn. Res. 22: 182:1-182:52 (2021) - [c23]Joe Watson, Jihao Andreas Lin, Pascal Klink, Joni Pajarinen, Jan Peters:
Latent Derivative Bayesian Last Layer Networks. AISTATS 2021: 1198-1206 - [c22]Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Convex Regularization in Monte-Carlo Tree Search. ICML 2021: 2365-2375 - [c21]Wenyan Yang, Nataliya Strokina, Nikolay Serbenyuk, Joni Pajarinen, Reza Ghabcheloo, Juho Vihonen, Mohammad M. Aref, Joni-Kristian Kämäräinen:
Neural Network Controller for Autonomous Pile Loading Revised. ICRA 2021: 2198-2204 - [c20]Lauri Alho, Adrian Burian, Janne Helenius, Joni Pajarinen:
Machine Learning Based Mobile Network Throughput Classification. WCNC 2021: 1-6 - [i19]Pascal Klink, Hany Abdulsamad, Boris Belousov, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learning. CoRR abs/2102.13176 (2021) - [i18]Wenyan Yang, Nataliya Strokina, Nikolay Serbenyuk, Joni Pajarinen, Reza Ghabcheloo, Juho Vihonen, Mohammad M. Aref, Joni-Kristian Kämäräinen:
Neural Network Controller for Autonomous Pile Loading Revised. CoRR abs/2103.12379 (2021) - [i17]Stephan Weigand, Pascal Klink, Jan Peters, Joni Pajarinen:
Reinforcement Learning using Guided Observability. CoRR abs/2104.10986 (2021) - 2020
- [j9]Mikko Lauri, Joni Pajarinen, Jan Peters:
Multi-agent active information gathering in discrete and continuous-state decentralized POMDPs by policy graph improvement. Auton. Agents Multi Agent Syst. 34(2): 42 (2020) - [j8]Mikko Lauri, Joni Pajarinen, Jan Peters, Simone Frintrop:
Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization. IEEE Robotics Autom. Lett. 5(4): 5323-5330 (2020) - [j7]Joni Pajarinen, Oleg Arenz, Jan Peters, Gerhard Neumann:
Probabilistic Approach to Physical Object Disentangling. IEEE Robotics Autom. Lett. 5(4): 5510-5517 (2020) - [c19]Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Generalized Mean Estimation in Monte-Carlo Tree Search. IJCAI 2020: 2397-2404 - [c18]Melvin Laux, Oleg Arenz, Jan Peters, Joni Pajarinen:
Deep Adversarial Reinforcement Learning for Object Disentangling. IROS 2020: 5504-5510 - [c17]Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Self-Paced Deep Reinforcement Learning. NeurIPS 2020 - [i16]Simone Parisi, Davide Tateo, Maximilian Hensel, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Long-Term Visitation Value for Deep Exploration in Sparse Reward Reinforcement Learning. CoRR abs/2001.00119 (2020) - [i15]Joni Pajarinen, Oleg Arenz, Jan Peters, Gerhard Neumann:
Probabilistic approach to physical object disentangling. CoRR abs/2002.11495 (2020) - [i14]Melvin Laux, Oleg Arenz, Jan Peters, Joni Pajarinen:
Deep Adversarial Reinforcement Learning for Object Disentangling. CoRR abs/2003.03779 (2020) - [i13]Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Self-Paced Deep Reinforcement Learning. CoRR abs/2004.11812 (2020) - [i12]Lauri Alho, Adrian Burian, Janne Helenius, Joni Pajarinen:
Machine Learning Based Mobile Network Throughput Classification. CoRR abs/2004.13148 (2020) - [i11]Tuan Dam, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Convex Regularization in Monte-Carlo Tree Search. CoRR abs/2007.00391 (2020) - [i10]Mikko Lauri, Joni Pajarinen, Jan Peters, Simone Frintrop:
Multi-Sensor Next-Best-View Planning as Matroid-Constrained Submodular Maximization. CoRR abs/2007.02084 (2020) - [i9]Joni Pajarinen:
Technical Report: The Policy Graph Improvement Algorithm. CoRR abs/2009.02164 (2020) - [i8]Joni Pajarinen, Jens Lundell, Ville Kyrki:
POMDP Manipulation Planning under Object Composition Uncertainty. CoRR abs/2010.13565 (2020)
2010 – 2019
- 2019
- [j6]Joni Pajarinen, Hong Linh Thai, Riad Akrour, Jan Peters, Gerhard Neumann:
Compatible natural gradient policy search. Mach. Learn. 108(8-9): 1443-1466 (2019) - [j5]Dorothea Koert, Joni Pajarinen, Albert Schotschneider, Susanne Trick, Constantin A. Rothkopf, Jan Peters:
Learning Intention Aware Online Adaptation of Movement Primitives. IEEE Robotics Autom. Lett. 4(4): 3719-3726 (2019) - [c16]Mikko Lauri, Joni Pajarinen, Jan Peters:
Information Gathering in Decentralized POMDPs by Policy Graph Improvement. AAMAS 2019: 1143-1151 - [c15]Riad Akrour, Joni Pajarinen, Jan Peters, Gerhard Neumann:
Projections for Approximate Policy Iteration Algorithms. ICML 2019: 181-190 - [c14]Samuele Tosatto, Carlo D'Eramo, Joni Pajarinen, Marcello Restelli, Jan Peters:
Exploration Driven by an Optimistic Bellman Equation. IJCNN 2019: 1-8 - [i7]Joni Pajarinen, Hong Linh Thai, Riad Akrour, Jan Peters, Gerhard Neumann:
Compatible Natural Gradient Policy Search. CoRR abs/1902.02823 (2019) - [i6]Mikko Lauri, Joni Pajarinen, Jan Peters:
Information Gathering in Decentralized POMDPs by Policy Graph Improvement. CoRR abs/1902.09840 (2019) - [i5]Zhang-Wei Hong, Joni Pajarinen, Jan Peters:
Model-based Lookahead Reinforcement Learning. CoRR abs/1908.06012 (2019) - [i4]Tuan Dam, Pascal Klink, Carlo D'Eramo, Jan Peters, Joni Pajarinen:
Generalized Mean Estimation in Monte-Carlo Tree Search. CoRR abs/1911.00384 (2019) - 2018
- [j4]Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters:
An Algorithmic Perspective on Imitation Learning. Found. Trends Robotics 7(1-2): 1-179 (2018) - [c13]Janine Hoelscher, Dorothea Koert, Jan Peters, Joni Pajarinen:
Utilizing Human Feedback in POMDP Execution and Specification. Humanoids 2018: 104-111 - [i3]Takayuki Osa, Joni Pajarinen, Gerhard Neumann, J. Andrew Bagnell, Pieter Abbeel, Jan Peters:
An Algorithmic Perspective on Imitation Learning. CoRR abs/1811.06711 (2018) - 2017
- [j3]Joni Pajarinen, Ville Kyrki:
Robotic manipulation of multiple objects as a POMDP. Artif. Intell. 247: 213-228 (2017) - [c12]Joni Pajarinen, Ville Kyrki, Michael C. Koval, Siddhartha S. Srinivasa, Jan Peters, Gerhard Neumann:
Hybrid control trajectory optimization under uncertainty. IROS 2017: 5694-5701 - [i2]Joni Pajarinen, Ville Kyrki, Michael C. Koval, Siddhartha S. Srinivasa, Jan Peters, Gerhard Neumann:
Hybrid control trajectory optimization under uncertainty. CoRR abs/1702.04396 (2017) - 2016
- [c11]Kevin Sebastian Luck, Joni Pajarinen, Erik Berger, Ville Kyrki, Heni Ben Amor:
Sparse Latent Space Policy Search. AAAI 2016: 1911-1918 - [c10]Mattia Racca, Joni Pajarinen, Alberto Montebelli, Ville Kyrki:
Learning in-contact control strategies from demonstration. IROS 2016: 688-695 - [c9]Aleksi Ikkala, Joni Pajarinen, Ville Kyrki:
Benchmarking RGB-D Segmentation: Toy Dataset of Complex Crowded Scenes. VISIGRAPP (4: VISAPP) 2016: 107-116 - 2015
- [c8]Joni Pajarinen, Ville Kyrki:
Decision making under uncertain segmentations. ICRA 2015: 1303-1309 - 2014
- [j2]Joni Pajarinen, Ari Hottinen, Jaakko Peltonen:
Optimizing Spatial and Temporal Reuse inWireless Networks by Decentralized Partially Observable Markov Decision Processes. IEEE Trans. Mob. Comput. 13(4): 866-879 (2014) - [c7]Joni Pajarinen, Ville Kyrki:
Robotic manipulation in object composition space. IROS 2014: 1-6 - [c6]Polychronis Kondaxakis, Joni Pajarinen, Ville Kyrki:
Real-time recognition of pointing gestures for robot to robot interaction. IROS 2014: 2621-2626 - [i1]Joni Pajarinen, Ville Kyrki:
Robotic manipulation of multiple objects as a POMDP. CoRR abs/1402.0649 (2014) - 2013
- [b1]Joni Pajarinen:
Planning under uncertainty for large-scale problems with applications to wireless networking ; Päätöksenteko epävarmuuden vallitessa suurissa ongelmissa ja sovelluksia langattomaan tiedonsiirtoon. Aalto University, Helsinki, Finland, 2013 - [c5]Joni Pajarinen, Jaakko Peltonen:
Expectation Maximization for Average Reward Decentralized POMDPs. ECML/PKDD (1) 2013: 129-144 - 2011
- [j1]Joni Pajarinen, Jaakko Peltonen, Mikko A. Uusitalo:
Fault tolerant machine learning for nanoscale cognitive radio. Neurocomputing 74(5): 753-764 (2011) - [c4]Joni Pajarinen, Jaakko Peltonen:
Efficient Planning for Factored Infinite-Horizon DEC-POMDPs. IJCAI 2011: 325-331 - [c3]Joni Pajarinen, Jaakko Peltonen:
Periodic Finite State Controllers for Efficient POMDP and DEC-POMDP Planning. NIPS 2011: 2636-2644 - 2010
- [c2]Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, Mikko A. Uusitalo:
Efficient Planning in Large POMDPs through Policy Graph Based Factorized Approximations. ECML/PKDD (3) 2010: 1-16
2000 – 2009
- 2009
- [c1]Joni Pajarinen, Jaakko Peltonen, Mikko A. Uusitalo, Ari Hottinen:
Latent state models of primary user behavior for opportunistic spectrum access. PIMRC 2009: 1267-1271
Coauthor Index
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last updated on 2023-05-23 21:28 CEST by the dblp team
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