Daniel Palenicek

I am a fifth-year PhD student at TU Darmstadt & hessian.AI supervised by Prof. Jan Peters. During my PhD I have been working on sample-efficient reinforcement learning for robotics. I was selected as a 2026-2027 NVIDIA Graduate Fellowship Finalist.

CV [03/2026] LinkedIn Scholar GitHub X

Short CV

2026⁠—Now Research Scientist Intern NVIDIA Seattle Robotics Lab

2026 Research Scientist Intern Meta FAIR, New York

2021⁠—Now PhD Student TU Darmstadt & hessian.AI

2019⁠—2020 Research Intern Huawei R&D London

2019 Research Intern Bosch Center for AI, Renningen

2015⁠—2021 M.Sc. Wirtschaftsinformatik TU Darmstadt

📄 Selected Publications

Diminishing Return of Value Expansion Methods

Palenicek D., Lutter M., Carvalho J., Dennert D., Ahmad F., Peters J.

IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 48, no. 11, pp. 14351-14361, 2026

Paper IEEE Xplore

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

Palenicek D.*, Vogt F.*, Watson J.*, Posner I., Kragic D., Peters J.

ICML 2026 Workshop on Decision-Making from Offline Datasets to Online Adaptation: Black-Box Optimization to Reinforcement LearningSpotlight

Paper

XQC: Well-Conditioned Optimization Accelerates Deep Reinforcement Learning

Palenicek D., Vogt F., Watson J., Posner I., Peters J.

ICLR 2026

Paper Project Page

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

Kim D.*, Lee Y.*, Park M., Kim K., Nahrendra I.M.A., Seno T., Min S., Palenicek D., Vogt F., Kragic D., Peters J., Choo J., Lee H.

RSS 2026Outstanding Paper Award

Paper Code Project Page

Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization

Palenicek D., Vogt F., Watson J., Peters J.

NeurIPS 2025

Paper

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

Bohlinger N.*, Kinzel J.*, Palenicek D., Antczak L., Peters J.

IROS 2025

Paper Project Page

Towards Safe Robot Foundation Models Using Inductive Biases

Palenicek D.*, Tölle M.*, Gruner T.*, Schneider T., Günster J., Watson J., Tateo D., Liu P., Peters J.

Safe-VLM Workshop @ ICRA 2025Spotlight & GRC 2025

Paper Project Page

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

Palenicek D.*, Bhatt A.*, Belousov B., Argus M., Amiranashvili A., Brox T., Peters J.

ICLR 2024Spotlight

Paper Code Webpage

Learning Tactile Insertion in the Real World

Palenicek D.*, Gruner T.*, Schneider T.*, Böhm A.*, Lenz J.*, Pfenning I.*, Krämer E., Peters J.

ICRA@40 & ViTac Workshop @ ICRA 2024

Paper

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

Palenicek D., Lutter M., Carvalho J., Peters J.

ICLR 2023

Paper Code

See Google Scholar for complete list.

🎤 Talks

Sample Efficiency in Deep RL: Quo Vadis? / link

BeNeRL Seminar Series, September 2024

CrossQ: Batch Normalization in Deep Reinforcement Learning / link

ML&AI Academy, February 2024

Slurm-tastic Adventures on the IAS Cluster / link

IWIALS (Workshop on Intelligent Autonomous Systems), August 2023

📰 Media

State of AI Report 2026: FlashSAC featured on slide 63 / link

Air Street Capital, October 2026

hessian.AI Ph.D. researcher Daniel Palenicek named finalist of the NVIDIA Graduate Fellowship Program / link

hessian.AI, December 2025

NVIDIA Awards up to $60,000 Research Fellowships to PhD Students / link

NVIDIA Blog, December 2025

Learn more efficiently, save resources / link

TU Darmstadt, December 2025

AI and its faces (interview) / link

hessian.AI, with Theo Gruner, August 2023