hessian.AI Ph.D. researcher Daniel Palenicek named finalist of the NVIDIA Graduate Fellowship Program
hessian.AI Ph.D. researcher Daniel Palenicek from the Intelligent Autonomous Systems (IAS) research group at TU Darmstadt has been selected as a finalist of the prestigious NVIDIA Graduate Fellowship Program. He is one of five finalists worldwide honored by NVIDIA for their outstanding research in computing innovation. In the summer of 2026, Palenicek will complete an internship at the NVIDIA Seattle Robotics Lab.
“For me, this is a great honor,” says Palenicek, “I am proud to be listed alongside other top students from around the world.“
Researching efficient learning for robots
Palenicek conducts research in the field of robot learning, a subdomain of AI that enables robots to learn tasks through interaction with their environment—similar to how humans learn through trial and error. His work focuses on making reinforcement learning algorithms more efficient, both in terms of computational resources and the number of interactions required with the environment. Since such interactions can be costly and time-consuming for real-world robots, efficiency is key.
Together with colleagues at IAS, Palenicek recently developed an algorithm that stabilizes and accelerates the training process of AI systems. The approach addresses the so-called “loss of plasticity,” a common issue where systems become resistant to new learning after extensive training. By combining two normalization techniques, the team was able to maintain learning ability and significantly improve data efficiency.
This research was presented in early December at NeurIPS 2025 in San Diego. The work is part of the Reasonable Artificial Intelligence (RAI) Cluster of Excellence, coordinated by TU Darmstadt and hessian.AI. RAI focuses on developing a new generation of AI systems that are resource-efficient, safe, and capable of continuous learning.
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Bridging research and application
As part of his fellowship application, Palenicek submitted a research proposal outlining approaches for the automatic and continuous improvement of so-called “robot foundation models.” These models, trained on large expert datasets, often suffer from limited generalization capabilities. Palenicek aims to develop methods that use reinforcement learning to efficiently and safely adapt such models to new tasks and robotic systems.
Palenicek appreciates that his research has been recognized by NVIDIA: “I’m very much looking forward to the internship. The invitation to intern at the Seattle Robotics Lab is a great opportunity to continue my research in an industrial setting and to experience cutting-edge developments in robot learning firsthand.”
For more information about the fellowship, please visit the NVIDIA blog post.
