Daniel Palenicek

According to our database, Daniel Palenicek authored at least 22 papers between 2020 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

XQCfD: Accelerating Fast Actor-Critic Algorithms with Prior Data and Prior Policies.

Daniel Palenicek, Florian Vogt, Joe Watson, Ingmar Posner, Danica Kragic, Jan Peters

CoRR, May, 2026

FlashSAC: Fast and Stable Off-Policy Reinforcement Learning for High-Dimensional Robot Control.

Donghu Kim, Youngdo Lee, Minho Park, Kinam Kim, I Made Aswin Nahendra, Takuma Seno, Sehee Min, Daniel Palenicek, Florian Vogt, Danica Kragic, Jan Peters, Jaegul Choo, Hojoon Lee

CoRR, April, 2026

StructSAM: Structure- and Spectrum-Preserving Token Merging for Segment Anything Models.

Duy M. H. Nguyen, Tuan A. Tran, Duong Nguyen, Siwei Xie, Trung Q. Nguyen, Mai T. N. Truong, Daniel Palenicek, An T. Le, Michael Barz, TrungTin Nguyen, Tuan Dam, Ngan Le, Minh Vu, Khoa D. Doan, Ngo Anh Vien, Pengtao Xie, James Zou, Daniel Sonntag, Jan Peters, Mathias Niepert

CoRR, March, 2026

2025

XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning.

Daniel Palenicek, Florian Vogt, Joe Watson, Ingmar Posner, Jan Peters

CoRR, September, 2025

Scaling CrossQ with Weight Normalization.

Daniel Palenicek, Florian Vogt, Jan Peters

CoRR, June, 2025

Towards Safe Robot Foundation Models Using Inductive Biases.

Maximilian Tölle, Theo Gruner, Daniel Palenicek, Tim Schneider, Jonas Günster, Joe Watson, Davide Tateo, Puze Liu, Jan Peters

CoRR, May, 2025

Towards Safe Robot Foundation Models.

Maximilian Tölle, Theo Gruner, Daniel Palenicek, Jonas Günster, Puze Liu, Joe Watson, Davide Tateo, Jan Peters

CoRR, March, 2025

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization.

Daniel Palenicek, Florian Vogt, Jan Peters

CoRR, February, 2025

Iterated <i>Q</i>-Network: Beyond One-Step Bellman Updates in Deep Reinforcement Learning.

Théo Vincent, Daniel Palenicek, Boris Belousov, Jan Peters, Carlo D'Eramo

Trans. Mach. Learn. Res., 2025

Gait in Eight: Efficient On-Robot Learning for Omnidirectional Quadruped Locomotion.

Nico Bohlinger, Jonathan Kinzel, Daniel Palenicek, Lukasz Antczak, Jan Peters

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2025

DIME: Diffusion-Based Maximum Entropy Reinforcement Learning.

Onur Celik, Zechu Li, Denis Blessing, Ge Li, Daniel Palenicek, Jan Peters, Georgia Chalvatzaki, Gerhard Neumann

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Diminishing Return of Value Expansion Methods.

Daniel Palenicek, Michael Lutter, João Carvalho, Daniel Dennert, Faran Ahmad, Jan Peters

CoRR, 2024

Analysing the Interplay of Vision and Touch for Dexterous Insertion Tasks.

Janis Lenz, Theo Gruner, Daniel Palenicek, Tim Schneider, Jan Peters

CoRR, 2024

Learning Tactile Insertion in the Real World.

Daniel Palenicek, Theo Gruner, Tim Schneider, Alina Böhm, Janis Lenz, Inga Pfenning, Eric Krämer, Jan Peters

CoRR, 2024

Iterated Q-Network: Beyond the One-Step Bellman Operator.

Théo Vincent, Daniel Palenicek, Boris Belousov, Jan Peters, Carlo D'Eramo

CoRR, 2024

CrossQ: Batch Normalization in Deep Reinforcement Learning for Greater Sample Efficiency and Simplicity.

Aditya Bhatt, Daniel Palenicek, Boris Belousov, Max Argus, Artemij Amiranashvili, Thomas Brox, Jan Peters

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Pseudo-Likelihood Inference.

Theo Gruner, Boris Belousov, Fabio Muratore, Daniel Palenicek, Jan R. Peters

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Diminishing Return of Value Expansion Methods in Model-Based Reinforcement Learning.

Daniel Palenicek, Michael Lutter, Joao Carvalho, Jan Peters

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

SAMBA: safe model-based & active reinforcement learning.

Alexander I. Cowen-Rivers, Daniel Palenicek, Vincent Moens, Mohammed Amin Abdullah, Aivar Sootla, Jun Wang, Haitham Bou-Ammar

Mach. Learn., 2022

Revisiting Model-based Value Expansion.

Daniel Palenicek, Michael Lutter, Jan Peters

CoRR, 2022

2020

SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving.

Ming Zhou, Jun Luo, Julian Villela, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Aurora Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Dong Chen, Zhengbang Zhu, Nhat M. Nguyen, Mohamed Elsayed, Kun Shao, Sanjeevan Ahilan, Baokuan Zhang, Jiannan Wu, Zhengang Fu, Kasra Rezaee, Peyman Yadmellat, Mohsen Rohani, Nicolas Perez Nieves, Yihan Ni, Seyedershad Banijamali, Alexander I. Cowen-Rivers, Zheng Tian, Daniel Palenicek, Haitham Bou-Ammar, Hongbo Zhang, Wulong Liu, Jianye Hao, Jun Wang

CoRR, 2020

SMARTS: An Open-Source Scalable Multi-Agent RL Training School for Autonomous Driving.

Ming Zhou, Jun Luo, Julian Villela, Yaodong Yang, David Rusu, Jiayu Miao, Weinan Zhang, Montgomery Alban, Iman Fadakar, Zheng Chen, Chongxi Huang, Ying Wen, Kimia Hassanzadeh, Daniel Graves, Zhengbang Zhu, Yihan Ni, Nhat M. Nguyen, Mohamed Elsayed, Haitham Ammar, Alexander I. Cowen-Rivers, Sanjeevan Ahilan, Zheng Tian, Daniel Palenicek, Kasra Rezaee, Peyman Yadmellat, Kun Shao, Dong Chen, Baokuan Zhang, Hongbo Zhang, Jianye Hao, Wulong Liu, Jun Wang

Proceedings of the 4th Conference on Robot Learning, 2020